Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=2&sort=funder
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=funder", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=funder", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=funder", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=funder" }, "data": [ { "type": "Grant", "id": "5475", "attributes": { "award_id": "0630969", "title": "Increasing Student Success in Biology & Biotechnology", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [], "start_date": "2007-01-01", "end_date": "2011-06-30", "award_amount": 495863, "principal_investigator": { "id": 19077, "first_name": "E. Eileen", "last_name": "Gardner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1411, "ror": "https://ror.org/00k3ayt93", "name": "William Paterson University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1411, "ror": "https://ror.org/00k3ayt93", "name": "William Paterson University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project provides 12 full scholarships each year over four years to academically talented, low-income Biology and Biotechnology majors, and supports faculty-guided research experiences, tutoring, internships and field trips to industry settings for the scholarship recipients. The project goal is to increase retention and graduation rates, including accelerating degree completion by providing the means for current part-time students to pursue full-time study. The intellectual merit of the project is the preparation of an increased number of well-qualified biologists and biotechnologists, and helping students with the desire to succeed overcome academic disadvantages. The project is led by a Project Director and a Head Mentor with over 50 years of combined experience in teaching, promoting and supervising student research. Its broader impacts are increasing the supply of trained Biology and Biotechnology technicians to meet the growing demand for them in New Jersey (which is home to one of the largest concentrations of pharmaceutical, chemical and other biotechnology-based industries in the world), the region and the nation; and increasing the number of individuals who are members of groups currently underrepresented in these fields earning B.S. degrees. In recent years, 65% of our Biology and Biotechnology majors have been women, 19% have been Hispanic and 17% have been African-American. The success of the project is measured by its impact on closing gaps in the retention and graduation rates for these students compared to the overall rates for our Biology and Biotechnology majors over the past five years. Project results are disseminated through presentations and reports at national and regional meetings, with individual student successes publicized through University publications and press releases to regional media outlets.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2500", "attributes": { "award_id": "2017789", "title": "Equity and Sustainability: A framework for Equitable Energy Transition Analyses", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EnvS-Environmtl Sustainability" ], "program_reference_codes": [], "program_officials": [ { "id": 7114, "first_name": "Bruce", "last_name": "Hamilton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2023-06-30", "award_amount": 399915, "principal_investigator": { "id": 7116, "first_name": "Destenie", "last_name": "Nock", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Decisions regarding transitions from traditional energy sources such as fossil fuels to more sustainable, renewable energy systems impact multiple constituencies, including the most vulnerable members of society. This research addresses two questions: (1) What are transition pathways from non-renewable energy sources (such as fossil fuels) to renewable energy sources (such as wind and solar) for the US electricity sector that can best balance the (sometimes conflicting) objectives of the transition, while accounting for social equity and sustainability? (2) How can transition to a low-carbon electricity system be done in a way that minimizes adverse impacts on the most vulnerable members of society? This research targets creating a new way to account for social equity in the sustainability analysis of transitions to new energy systems, which may help guide decision-makers. \n\nThere are many decision makers and constituencies in energy system planning, each of which may make decisions or influence decisions according to their own versions of the desired goals. This research builds and expands upon previous research in three key ways that permit a more robust sustainability assessment of future electricity systems, and incorporates social equity into the energy transition discussion. First, an electricity system expansion model is coupled with a system sustainability model and then examined to ask how increasing carbon constraints are likely to impact power system development, and how important regional cooperation is likely to be in achieving a fully decarbonized US electricity system. Second, social equity will be an integral part of the sustainability analysis framework, thus displaying how other facets of sustainability impede or support an equitable energy transition. Third, to illuminate the social equity trade-offs, how regional cooperation may impact job and price equity around the country will be investigated. This research will be a system sustainability analysis for the entire US that incorporates multiple metrics for social equity, while capturing impacts of integrating intermittent renewables in the grid. The PI will develop an open-source data analysis tool for electricity sustainability analysis, enriching the discussion and uncovering the interactions among sustainability criterion at a national scale. The social equity focused framework is targeted to facilitate national discussions about how energy transition will impact communities in the US. This framework may also help support planning for job recovery of those most affected by the retirement of fossil fuel generation.\n\nThis 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": "5302", "attributes": { "award_id": "0819132", "title": "PASI: Cutting-edge Topics in Theoretical Statistics and Applications in Genetics and Bioinformatics; Guanajuato, Mexico, June-July 2009", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "AMERICAS PROGRAM" ], "program_reference_codes": [], "program_officials": [], "start_date": "2008-09-01", "end_date": "2011-08-31", "award_amount": 99025, "principal_investigator": { "id": 18647, "first_name": "Javier", "last_name": "Rojo", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 357, "ror": "", "name": "William Marsh Rice University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 357, "ror": "", "name": "William Marsh Rice University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This Pan-American Advanced Studies Institutes (PASI) award, jointly supported by the NSF and the Department of Energy (DOE), will take place from June 19 to July 17, 2009 at the Centro de Investigación en Matemáticas (CIMAT) in Guanajuato, Mexico. Organized by Dr. Javier Rojo of Rice University, the PASI will address cutting edge topics in theoretical statistics and applications to genetics and bioinformatics. Top researchers from Costa Rica, Mexico, Uruguay, and the United States will present cutting edge research in the areas of Statistical Finance, Statistical Multivariate Methods, Dimension Reduction, Survival Analysis with Microarray Data, Bioinformatics, and Statistical Genetics. \n\nThe activity will provide 45 young researchers (including advanced PhD students, post-docs, and young faculty) with support for the duration of the Institute. Expected outcomes in this PASI will include: re-energized efforts in Latin America in theoretical statistics and their applications, enhanced collaborations between U.S. and Latin American researchers, and increased student exposure and experience to new fields of knowledge. The PASI results will be disseminated through lecture notes and proceedings to be published through the CIMAT and Rice University technical report series and will be mailed to all the participants. The lecture notes will also be made available on-line.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2516", "attributes": { "award_id": "1955883", "title": "III: Medium: Collaborative Research: Detecting and Controlling Network-based Spread of Hospital Acquired Infections", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [ { "id": 7179, "first_name": "Sylvia", "last_name": "Spengler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2023-05-31", "award_amount": 416000, "principal_investigator": { "id": 7180, "first_name": "B Aditya", "last_name": "Prakash", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Hospital Acquired Infections (HAIs) are becoming a major challenge in health systems worldwide. Detection and control of HAIs are challenging and resource intensive, because of the high costs of patient treatment and disinfection of hospital facilities, making them fundamental public health problems. Despite its huge importance for hospitals, and the interest from both clinical and epidemiological researchers, these problems remain poorly understood. This project seeks to develop a novel network-based approach to improve hospital infection control using models and data science. This proposal brings together a highly multi-disciplinary team of researchers, and will lead to fundamental contributions in different areas of computer science (data mining, machine learning, graph mining, social networks, and optimization), network science (mathematical models and dynamical systems) and computational epidemiology (infectious diseases, and hospital epidemiology). The planned work has immediate implications for public health e.g. it can lead to new design policies and guidance for hospital infection control. Research findings will be incorporated into graduate level classes, tutorials, contests and workshops to bring computational biologists and data miners together. \n\nThere are several challenges in studying HAI outbreaks primarily because the dynamics of HAI spread are much more complex than other diseases, such as influenza, due to many more factors and pathways involved. To overcome these issues, the project team will use a new class of two-mode cascade models, which have very different dynamics than the standard models, and have not been studied in data mining. The will investigate the following topics: (1) Surveillance, early detection of HAI outbreaks, (2) Designing interventions to control the spread of HAIs, and (3) Modeling and estimating exposure risk for HAIs. A unified set of problems will be considered, including modeling, detection, control and inference of missing infections. These are challenging stochastic optimization problems on networks, and the project team will invent rigorous and scalable methods using tools from data mining, machine learning and combinatorial optimization. Their research will use a unique fine-grained, large-scale data set of operations from a public hospital, supplemented with data from other hospitals. The results will be validated with the help of domain experts including epidemiologists and clinicians involved in hospital infection control.\n\nThis 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": "10403", "attributes": { "award_id": "2149551", "title": "Deep learning-based serological test for point-of-care analysis of COVID-19 immunity with a paper-based multiplexed sensor", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-15", "end_date": "2025-08-31", "award_amount": 393222, "principal_investigator": { "id": 4172, "first_name": "Aydogan", "last_name": "Ozcan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Deep learning-based serological test for point-of-care analysis of COVID-19 immunity with a paper-based multiplexed sensor\n\nAbstract: \n\nCOVID-19, caused by the virus SARS-CoV-2, was declared a pandemic by the World Health Organization (WHO) on March 12, 2020. Diagnostic testing has been a critical focus of the response, with an urgent need to rapidly develop, scale, and distribute new tests. Despite all the successful testing methods developed for the direct detection of SARS-CoV-2 genetic material, there is still an urgent need to create new serological assays that can detect virus-specific antibodies as they can ascertain complementary information to direct detection methods by indicating previous exposure and potential immunity, especially important due to various emerging variants. In addition, as vaccines against new variants roll out, these serological tests can be used to evaluate the efficacy of vaccination campaigns, including the ability to elicit SARS-CoV-2 and variant antigen-specific antibodies across vaccinated and unvaccinated populations. In contrast to the current direct detection methods, serology tests that detect antibodies can be low-cost and conducive to a point-of-care (POC) setting, enabling broad screening efforts like widespread immunity testing to indicate individuals in need of vaccine boosters, qualify individuals for travel, return to work, and/or identify convalescent plasma donors. To serve this urgent need, this project will create a smartphone-based, cost-effective platform that can sense and measure the many different antibodies specific to SARS-CoV-2 a person may develop, in a testing format that is easy to use and can be completed within 15 min using an inexpensive paper-based test. \n\nThe team of researchers will develop a multiplexed POC immunoassay and serodiagnostic algorithm that will infer the vaccination/immunity status from up to 10 unique immunoreactions to distinguish an array of SARS-CoV-2 antibodies. For this, the research team will create a multiplexed vertical flow assay (xVFA) to simultaneously detect IgA, IgM, and IgG antibodies to the S protein (as well as variants of the S protein, such as delta, lambda, and other emerging variants), with separate immunoreaction sites dedicated to S-1, S-2, and the receptor-binding domain (RBD) of the S-protein in the SARS-CoV-2 virus and its most recent variants. Using existing and de-identified human serum samples, with the xVFA platform, the research team will screen COVID-19-positive samples, including those resulting from common variants (confirmed through reverse transcriptase-Polymerase Chain Reaction and sequencing) along with vaccinated samples and pre-pandemic un-vaccinated negative control samples. A neural network will then be trained using quantitative information from the multiplexed immunoreactions and the ground-truth clinical state over a set of remnant human serum samples. This training phase will (1) create a serodiagnostic algorithm to identify a positive immune response to SARS-CoV-2 infection (including common variants) or vaccination status using the multiplexed antibody measurements, and (2) identify the key subset of antibody-antigen interactions that most accurately represent and quantify an immune response to SARS-CoV-2 infection or protection via vaccination. A blinded testing phase will benchmark the performance enhancement of the multiplexed and data-driven approach to rigorously validate the trained inference network's generalization. By validating a new multiplexed vertical flow assay and serodiagnosis algorithm for COVID-19 immune protection, the research team aims to determine the significant improvements in sensitivity and specificity gained through the multiple measurements and computational analysis, which come with little added cost or operational steps, or required sample volume. This project will also establish a complementary educational outreach program that will involve (1) public interviews and popular science articles in news media and the internet; (2) undergraduate research opportunities involving underrepresented students; and (3) graduate student training through the organization of workshops, seminars and conferences.\n\nThis 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": "10767", "attributes": { "award_id": "2117496", "title": "MRI: Acquisition of an EEG for Cognitive Research", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Major Research Instrumentation" ], "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": "2021-09-01", "end_date": "2022-08-31", "award_amount": 70404, "principal_investigator": { "id": 26835, "first_name": "Erik", "last_name": "Benau", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26834, "first_name": "Lorenz S", "last_name": "Neuwirth", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1249, "ror": "https://ror.org/02rrhsz92", "name": "SUNY College at Old Westbury", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This project supports the acquisition of an integrated electroencephalography (EEG) system (ActiCHamp 64 active-electrodes, Brain Products GmbH) to establish the first EEG laboratory at The State University of New York at Old Westbury (SUNY-OW). With 60-70% of the student body identifying as coming from a historically underrepresented minority population, SUNY-OW is the most diverse campus within the 64-campus SUNY system and one of the most diverse campuses in the United States. EEG is a non-invasive neuroimaging technique that allows the monitoring of human cerebral activity with millisecond precision while neurocognitive processes unfold in real-time. When these cognitive processes are time-locked to an event and then averaged over multiple trials, the resulting event-related potentials (ERPs) can provide rich temporal and spatial data related to when and where neurocognitive processes occur. The EEG laboratory at SUNY-OW is critical in developing cutting-edge research in cognitive, affective, and social neuroscience to educate and train its students. This award enables a unique initiative to establish an interdisciplinary research program in electrophysiology and cognitive neuroscience that includes several researchers in biological, cognitive, developmental, social, and clinical psychology, gerontology, and other related fields. The EEG laboratory will create a unique research and training consortium between SUNY-OW and surrounding campuses, including The New York Institute of Technology, SUNY at Farmingdale, Long Island University, and local community colleges and high schools. By providing cutting-edge neuroscience research and training opportunities for SUNY-OW’s undergraduate and graduate students, researchers will increase predominantly underrepresented students skills in psychophysiology, neuroscience, and other domains of science, technology, engineering, and mathematics (STEM). These skills are highly desirable and transferrable across disciplines spanning industry and academia. \n\nThe research focuses on four areas of interest related to emotional and social cognitive processing. First, the team analyzes early, response-locked components that are associated with attention and task engagement to better understand symptoms of depression and anxiety. Second, the investigators investigate whether the subjective ability to sense the body’s internal states is associated with improved senses of “self,” a key predictor of physical and psychological well-being. Third, the team examines factors influencing cognitive flexibility (i.e., the ability to respond to changes in the environment and/or within tasks) and its association with academic achievement in college. Finally, the researchers examine the difference in internalized stereotypes about aging in younger adults compared to older adults. Although previous behavioral and self-report data provide important, foundational insight into the cognitive processes of interest, the team's analyses provide the opportunity to delineate early attentional orienting cognitive lower-order processes from later, more effortful and self-oriented cognitive higher-order processes associated with memory and emotion activation. Therefore, the research provides a fine-grained and systematic approach to the cognitive mechanisms that underlie these psychological phenomena and further contribute to the field’s understanding of biopsychosocial and socioemotional processes that can predict multiple domains of wellbeing across the lifespan.\n\nThis 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": "10759", "attributes": { "award_id": "2236302", "title": "NSF Convergence Accelerator Track J: Data-driven Agriculture to Bridge Small Farms to Regional Food Supply Chains (L02619644)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "Convergence Accelerator Resrch" ], "program_reference_codes": [], "program_officials": [ { "id": 26489, "first_name": "Michael", "last_name": "Reksulak", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-12-15", "end_date": "2023-11-30", "award_amount": 743651, "principal_investigator": { "id": 26825, "first_name": "Meredith", "last_name": "Adkins", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [ { "id": 26821, "first_name": "Chase", "last_name": "Rainwater", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26822, "first_name": "Kristen E", "last_name": "Gibson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26823, "first_name": "Ngan H", "last_name": "Le", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26824, "first_name": "Yasser M", "last_name": "Sanad", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 586, "ror": "", "name": "University of Arkansas", "address": "", "city": "", "state": "AR", "zip": "", "country": "United States", "approved": true }, "abstract": "Global climate change and pandemic have exposed vulnerabilities in the globalized food system, exacerbating food insecurity, especially for diverse and underserved communities who already experience disproportionate access to safe and affordable food and nutrition. The need for resilient local food supply has refocused efforts on domestic sourcing of food in the United States. Yet, logistical and market knowledge barriers limit the viability of productive local food systems. The convergence of multiple scientific research fields and modern technological innovations such as artificial intelligence and machine learning can improve supply and demand efficiencies by extending small farmers’ access to market insights. This project will empower regional food producers to understand the economic value of the specialty crop assortment and food animals on their farms in comparison to market demand for institutional sales (e.g., retailers, food hubs, distributors, grocers, restaurants, hospitals, schools or colleges). The end-platform’s data-driven market and financial insights will enhance regional food producers’ abilities to obtain procurement contracts with institutional buyers, supporting local farms to bring their products to store shelves, restaurant tables, and cafeterias. \nAddressing the challenges of regional food systems will have broad societal implications for the economic livelihoods of small farmers and local businesses, and for the increased availability of safe and nutritious local food that will support metabolic health, particularly for disadvantaged communities. Furthermore, enhanced knowledge of sales channels will reduce food losses and enhance crop diversity, thus creating income streams for farmers practicing climate-friendly regenerative agricultural techniques such as mixed farming and crop diversification. Ultimately, this project advances the health and prosperity of the United States’ population, as well as environmental stewardship, through its focus on food and nutrition security. \n\nThis project assesses user needs to design a scalable technology platform that provides market insights to small farmers. The primary research objectives are: (a) to understand the knowledge barriers that small farmers experience to sell to institutional markets; and (b) to converge use-inspired research of multiple scientific disciplines and novel data-driven techniques to develop the conceptual design for a software platform that would support small farmers to access the relevant market information. The research methods include: (a) user discovery with small farmers and other stakeholders about the barriers that they face; (b) market analysis; (c) data collection that contributes to the conceptual design and data feeds for computational models in the platform. Data collection includes: (i) inventory assessments and interviews with institutional buyers in the underserved pilot regions to identify local demand for food products; (ii) product-level data collected on-farm via robotics, remote sensing, satellite data or drone, including both existing datasets and data collected from growers; (iii) assessment of low-cost validated on-farm preventative controls and detection of microbial risk for analysis of food safety economic risk models to support production decisions. \n\nCentral to the translation of research discovery to market impact, this project will identify the barriers that small farmers experience to understand institutional market demand and sell to institutional buyers. By identifying where gaps in knowledge contribute to supply and demand inefficiencies, it will also extend understanding of the data across scientific fields that could be integrated to inform business decisions, leveraging artificial intelligence (AI) and machine learning (ML) techniques, such as computer vision, to price farm products and create predictive models to anticipate future food demand and pricing. This work will advance the field for data-driven agriculture for small producers, supporting their livelihoods and local economic growth and food security.\n\nThis 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": "10741", "attributes": { "award_id": "2134366", "title": "GSA Penrose Conference: PRF2022 Progressive Failure of Brittle Rocks; Western North Carolina; June 2022", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "ECI-Engineering for Civil Infr" ], "program_reference_codes": [], "program_officials": [ { "id": 9152, "first_name": "Giovanna", "last_name": "Biscontin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2022-09-30", "award_amount": 32422, "principal_investigator": { "id": 26786, "first_name": "Martha", "last_name": "Eppes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 248, "ror": "https://ror.org/04dawnj30", "name": "University of North Carolina at Charlotte", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The 2022 Penrose Conference of the Geological Society of America on progressive rock failure (PRF2022) will bring together approximately 75 international scientists and engineers to facilitate a step-change in our understanding of rock fracture in both built and natural environments. To fully understand rock fracture is to understand elements of Earth that are critical for societal health and safety including: natural hazards related to rockfalls and landslides; degradation of rock-related infrastructure; rock weathering and erosion; and even the size and distribution of sedimentary particles and the internal structure – architecture - of Earth’s uppermost crust, which contribute to Earth’s soils and hold its water and natural resources. Yet, traditionally geoscientists studying natural rock fracture have largely overlooked the knowledge and implications to be derived from rock physics and engineering research, and vice versa. Therefore, a key goal of PRF2022 is to open doors to new insights that may be gained by studying progressive rock failure in the context of geotechnical engineering, surface processes, natural hazards and stone heritage problems. This goal will be achieved by maximizing both disciplinary diversity and participant diversity of the conference. To ensure longevity and accessibility of conference’s outcomes, PRF2022 will also provide a mentoring scheme for early career and student attendees and communicate the conference ideas with the public and among scientists themselves. \n\nThere is a burgeoning recognition of 1) the central role that fractures play in Critical Zone processes, landscape evolution, and geotechnical problems; and 2) a ‘new’ realm of fracture mechanics that applies to both engineered and natural surface processes but has been understudied in those contexts - namely progressive rock failure (a.k.a. subcritical cracking, environmental cracking and microcracking). In particular, the role of progressive rock failure in natural rock fracture has been largely unrecognized or misunderstood across a wide array of both surface process and engineering applications, where it likely plays a dominant role. To best leverage recent, rapidly evolving cross-discipline conceptualizations of rock fracture and its relationship to erosion, hazards, preservation, climate, and chemical weathering, this Penrose Conference will be held in Western North Carolina, USA (June 2022). PRF2022 will: provide a platform to identify complementary data/observations and modelling approaches; reveal key existing and needed datasets; stimulate new multidisciplinary collaborations and scientific and commercial funding opportunities; and build a framework for future evaluation of PRF within both academic and applied Earth sciences questions.\n\nThis 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": "10451", "attributes": { "award_id": "2200161", "title": "PIPP Phase I: Computational Foundations for Bio-social Modeling of Unseen Pandemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "PIPP-Pandemic Prevention" ], "program_reference_codes": [], "program_officials": [ { "id": 1123, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2024-02-29", "award_amount": 897531, "principal_investigator": { "id": 26449, "first_name": "Pavan", "last_name": "Turaga", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26445, "first_name": "Giulia", "last_name": "Pedrielli", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26446, "first_name": "Gautam", "last_name": "Dasarathy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26447, "first_name": "Visar", "last_name": "Berisha", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26448, "first_name": "Patricia A", "last_name": "Solis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Pandemics unfold in a social, behavioral, and decision-making context that alters the geospatial patterns of spread, depending on an existing underlying landscape of risk and adaptive behavior. Layering socioeconomic factors into traditional predictive modeling frameworks is not sufficient to understand this complexity, nor does it account for the dynamics and vicissitudes of human behavior and free-will. Unexpected human behaviors play a major role, as well as broader factors such as vaccine availability, seasonal effects from human contact patterns, viral environmental persistence, and federal and state-level policy changes around masking and business closures. Enumerating a finite list of factors that should form the basis of a predictive model itself seems like a grand challenge. This project will advance modeling as a continuous, iterative, and dynamic component of pandemic response, where incremental predictions are far more robust, and approaches that innately allow for complexity, adaptation, and surprise can be expected to be operationally useful.\n\nPandemic prevention for unseen pandemics requires several interconnected efforts across immunology, mechanistic modeling, data-driven modeling, and understanding sociopolitical contexts of decision making. Technical aspects of the project include machine learning based tools for predicting immune response from pathogen mutations, switching dynamical systems based models of time-series for fast adaptation, adaptive population sampling techniques, and model predictive control methods for designing behavioral interventions. The project will develop integrative protocols and frameworks that a) leverage techniques for using binding patterns of pathogens for never-before-seen viruses and advances in wastewater-based epidemiology, b) understand the variation in performance of predictive models over geospatial scales using regularizing models, c) design effective interventions under resource constraints, and d) understand their impact on policy making. \n\nThis award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Social, Behavioral and Economic Sciences (SBE) and Engineering (ENG).\n\nThis 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": "5286", "attributes": { "award_id": "0810026", "title": "SBIR Phase I: Highly Efficient CdTe Thin Film Solar Cells with Ordered Structure", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [], "start_date": "2008-07-01", "end_date": "2008-12-31", "award_amount": 99998, "principal_investigator": { "id": 18613, "first_name": "Lisen", "last_name": "Cheng", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 1389, "ror": "", "name": "NanoGreen Solutions Corporation", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1389, "ror": "", "name": "NanoGreen Solutions Corporation", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "This Small Business Innovation Research (SBIR) Phase I project addresses an innovative device fabrication process and bulk hetero-junction structure to fabricate CdTe solar cells with unprecedented performance. Today's crystalline silicon based solar cell technologies are not cost effective as a viable alternative to existing energy sources. CdTe based solar cells have shown very promising as a low cost alternative to current crystalline silicon solar cells. However, the energy conversion efficiency of commercial CdTe solar cells is only ~ 10%. With this innovative approach it is intended to improve energy conversion efficiency up to the limit efficiency of ~29% for CdTe based solar cells, representing a real breakthrough in thin film solar cells and leading to tremendously wide applications.\n\nWorld solar photovoltaic (PV) market installations reached a record high of 1,744 megawatts (MW) in 2006, representing growth of 19% over the previous year. World solar cell production reached a consolidated figure of 2,204 MW in 2006, up from 1,656 MW a year earlier. Global industry revenues were $10.6bn in 2006. According to a new report from Solarbuzz, LLC, annual worldwide industry revenues will reach between $18.6bn and $31.5bn by 2011. Currently, the solar cell market is dominated by crystalline silicon solar cells with a market share of ~93%. If successful the proposed approach can improve the energy efficiency of CdTe based solar cells to the next level, which enables them to compete with (even outperform) current crystalline silicon solar cells. With improved efficiency and low cost, CdTe solar cells will get a significant share of the solar market. There is an extensive range of applications where solar cells are already viewed as the best option for electricity supply such as ocean navigation aids, telecommunication systems, remote monitoring and control, rural electrification, space power and domestic power supply. The proposed green technology harvests solar energy, reducing the emission of CO2 and global warming. This program also provides a route to enhance scientific and technological understanding of crystal growth process at the nano-scale.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 2, "pages": 1392, "count": 13920 } } }