Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=funder_divisions
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=funder_divisions", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1442&sort=funder_divisions", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=funder_divisions", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=funder_divisions" }, "data": [ { "type": "Grant", "id": "1106", "attributes": { "award_id": "2112096", "title": "SBIR Phase II: An AI-powered wearable system and platform for long-term remote monitoring of pulmonary function (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 2771, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-15", "end_date": "2023-07-31", "award_amount": 958275, "principal_investigator": { "id": 2772, "first_name": "Maria", "last_name": "Artunduaga", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 523, "ror": "", "name": "Respira Labs, Inc.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 523, "ror": "", "name": "Respira Labs, Inc.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve care for pulmonary conditions, such as Chronic Obstructive Pulmonary Disease (COPD). This disease affects 30 million Americans, kills more than 150,000 a year and is the third leading cause of U.S. deaths. COPD costs Americans nearly $72 billion a year, half of which is spent on ER visits and hospital stays resulting from respiratory crises. Hospitalized COPD patients have reduced quality of life, more rapid lung disease progression, and a 50% increase in mortality in the subsequent two years. Currently, ongoing monitoring of COPD lung deterioration relies largely on patient-reported symptoms and inadequate tools (65-70% accurate). As a result, half of COPD deterioration remains undetected. The proposed technology facilitates early diagnosis and treatment of COPD attacks by continuous remote tracking of lung function, thereby preventing unnecessary ER visits and hospitalizations. This device improves care for long-term conditions like COPD or asthma, but also those with relatively quick onset, such as COVID-19.The proposed project advances translation of a device using acoustic resonance to measure lung air volume. The project develops an expanded data set for analysis and deployment at scale. The project further incorporates advanced classification models into the analysis workflow to track and predict deteriorating lung function in a cloud-computing environment. The project will also develop a version to run on a patient's mobile device to enable real-time feedback.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": "1109", "attributes": { "award_id": "2033865", "title": "SBIR Phase I: Desktop Plug-and-Play Vaporized Hydrogen Peroxide Sterilizers for Personal Protective Equipment (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 2777, "first_name": "Anna", "last_name": "Brady", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-15", "end_date": "2022-05-31", "award_amount": 255998, "principal_investigator": { "id": 2778, "first_name": "Michael C", "last_name": "Kimble", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 524, "ror": "", "name": "SKYHAVEN SYSTEMS, LLC", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 524, "ror": "", "name": "SKYHAVEN SYSTEMS, LLC", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to enable sterilization of personal protective equipment (PPE). Global response measures to the COVID-19 pandemic have highlighted severe shortages of N95 masks and PPE, which could be alleviated by sterilizing the equipment. This has been demonstrated effectively in large trailers and room-size facilities, but these large units are cost more than $1 M each, require substantive facility modifications, and have strict operational requirements. An alternative approach is to provide desktop sterilizers resembling the ubiquitous steam sterilizers typically used in hospitals and clinical settings. This project will develop a desktop plug-and-play vaporized hydrogen peroxide sterilizer unit to decontaminate PPE within 30 minutes at low cost. These could be deployed throughout a medical facility without requiring substantive room remodeling or restricted access rooms. This SBIR Phase I project proposes to develop and demonstrate a desktop plug-and-play vaporized hydrogen peroxide sterilizer. Vaporized hydrogen peroxide (VHP) is a proven sterilant that exhibits broad-spectrum efficacy against viruses, bacteria, yeasts and bacterial spores. Conventional VHP sterilizers are large units that use concentrated H2O2 cartridges to produce the requisite 750 ppm VHP concentration. Because these units use concentrated H2O2 canisters, the resulting systems have significant process controls and operations to maintain safe operation, design consequences that make it difficult to scale the technology to smaller economical desktop units. Toward producing safer desktop sterilization units, a new chemical reactor will be developed in this program that can produce hydrogen peroxide directly in the vapor state without using hazardous compounds or dangerous reaction conditions. Optimization of the catalysts, reaction area, and operating conditions will be conducted to produce vaporized hydrogen peroxide at concentrations upwards of 750 ppm. Additional components will be developed to safely operate the sterilizer including H2O2 concentration and relative humidity sensors, and a catalytic H2O2 decomposition reactor to breakdown residual H2O2 to water and oxygen. These components will be integrated into a desktop sterilizer unit, along with new control algorithms for safe operation.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": "1111", "attributes": { "award_id": "2054629", "title": "SBIR Phase II: Actionable Learning Analytics for the Classroom", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 2783, "first_name": "Diane", "last_name": "Hickey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2023-08-31", "award_amount": 945159, "principal_investigator": { "id": 2784, "first_name": "Douglas", "last_name": "Lare", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 525, "ror": "", "name": "LEARNICS, LLC", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 525, "ror": "", "name": "LEARNICS, LLC", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is the development of practical methods of collecting, analyzing and utilizing student online learning activity data to improve teaching and learning. COVID-19 caused an accelerated adoption of educational technologies. The new normal of education may be much more infused with technology. This project provides the analytic tools for educators to get the data insights necessary to design effective digital learning experiences and fully support students. Data analytics provide immense value to many online commercial organizations and businesses. These commercial analytics are used to better understand customers, promote desired customer behavior, predict future interactions and support many organizational decisions. This project seeks to make these insights available to educators and students. There are over three million teachers in the United States who currently have no practical way of reaping the value of analytics to gain insight into the online experiences that they are designing for their own students. The objective of this project is to provide teachers with the analytics and insights necessary to hold students accountable, provide instructional support, and design effective digital lessons. This Small Business Innovation Research (SBIR) Phase II project will explore ways to create automated processes that extract key data from student online learning activity records. In turn, the project seeks to translate this data into useful analytics that will offer teachers insights into students’ online learning experience. This project may improve the collection and display of targeted online learning analytics that provide educators with critical information about how students interact with online content. These advances will be coupled with doctoral research studies at East Stroudsburg University to collect and analyze student online learning data in order to create automated “Key Learning Indicators” and “Learning Experience Scores.” This project will work toward creating an industry standard for how student online learning data should be ethically collected, analyzed, and presented to educators.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": "1130", "attributes": { "award_id": "2125984", "title": "SBIR Phase I: Digital therapeutic virtual reality tool to address social isolation and improve geriatric care (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 2847, "first_name": "Diane", "last_name": "Hickey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-15", "end_date": "2022-07-31", "award_amount": 272082, "principal_investigator": { "id": 2848, "first_name": "Richard E", "last_name": "Hoagland", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 528, "ror": "", "name": "RALPHVR INC.", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 528, "ror": "", "name": "RALPHVR INC.", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a technology to support social-emotional health by fostering co-presence, defined as a sense of being together in a virtual environment analogous to person-to-person physical interaction. Loneliness can be debilitating and have detrimental impact on patients aged 65 and over, causing an estimated $7 billion in Medicare costs associated with the effects of morbidity and mortality factors, such as dementia, heart disease, stroke, and depression. The recent COVID-19 pandemic has highlighted both the challenges and opportunities of digital tools in fostering connections associated with mental and physical health. The proposed virtual reality solution and the emerging digital therapeutics sector can address significant challenges faced by hospitals, assisted living care facilities, and mental health facilities, improving clinical outcomes. The findings of this project are expected to impact other human interaction contexts, such as training, education, and others.This Small Business Innovation Research (SBIR) Phase I project advances translation of digital therapeutic tools to foster co-presence. The project compares physiological and psychological markers between pairs of individuals in both real and virtual contexts in search of significant markers for co-presence. Objectives include development of a custom virtual reality application and collection of quantitative data on pairs of individuals interacting in three social contexts. Proposed research will evaluate the degree to which virtual reality fosters co-presence and focus on specific algorithms and design choices as significant drivers of co-presence.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": "1136", "attributes": { "award_id": "2126780", "title": "SBIR Phase II: Adaptive Experiential Learning Platform to Close Learning Gaps in Secondary Algebra", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 2863, "first_name": "Diane", "last_name": "Hickey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2023-08-31", "award_amount": 944532, "principal_investigator": { "id": 2864, "first_name": "Anurupa", "last_name": "Ganguly", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 530, "ror": "", "name": "PRISMS OF REALITY INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 530, "ror": "", "name": "PRISMS OF REALITY INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project seeks to close gaps in Science, Technology, Engineering, and Mathematics (STEM) proficiency, improving participation and sustained achievement, particularly for historically underserved student populations. Learning science has validated that the mathematical sciences are processed experientially — through seeing, moving, doing, perceiving — but current educational solutions generally do not explore this learning modality. This platform will utilize Immersive Virtual Reality (IVR) and advanced analytics to scale these best practice pedagogies for both students and teachers. The aim of this project is to actualize the vision of technology-assisted STEM learning and operationalize it across the US K-12 system to close long-standing barriers to access and proficiency at scale. In the wake of the COVID-19 pandemic, this platform offers a way to rapidly close significant learning gaps students are facing.This Small Business Innovation Research (SBIR) Phase II project seeks to demonstrate how multimodal and game-driven learning in IVR can be used to enhance math proficiencies and the ability to transfer mathematical knowledge across contexts. Such abilities have so far eluded incumbent learning technologies and interventions for math. In particular, this research may culminate in an experiential solution for students to learn Algebra efficiently, effectively, and purposefully. Immersed in relevant real-world contexts, students will use tactile 3D tools, movement, and a research-based pedagogical framework to build lasting fluency with math concepts while solving high impact problems. The platform will leverage the latest advances in data analysis to personalize learning experiences based on individual behaviors, skills and goals. This solution provides teachers with real-time data on student thinking so they can closely monitor, identify, and support priority students while providing timely feedback during critical cognitive moments.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": "1181", "attributes": { "award_id": "2051411", "title": "SBIR Phase I: Rapid Solution to COVID-19 and Future Viral Pandemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 3013, "first_name": "Muralidharan", "last_name": "Nair", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2022-02-28", "award_amount": 255973, "principal_investigator": { "id": 3014, "first_name": "Timothy", "last_name": "Childs", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 539, "ror": "", "name": "TLC MILLIMETER WAVE PRODUCTS INC.", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 539, "ror": "", "name": "TLC MILLIMETER WAVE PRODUCTS INC.", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Research Innovation (SBIR) Phase I project is a demonstration of a millimeter wave Rapid Pathogen Detector System (or “RPDS”) to autonomously detect and instantly identify pathogens at tiny concentrations as even these samples may cause health threats to individuals or to a community. The “in the field” demonstration utilizes a database of captured and learned the characteristics (i.e., electronic signature) of the pathogen using a unique machine learning/artificial intelligence model. Once the RPDS is “trained” to recognize the pathogen, the RPDS may detect and identify pathogens in human and non-human samples in the field with high accuracy. The model target pathogen of this project is the SARS-CoV-2 virus. This target is to be followed by a selection of other viruses, proteins, biomatter, defective cells (cancer), and small inanimate objects as well. The RPDS system capabilities seek to increase the testing rate by an orders of magnitude compared to the current state of the art detection and diagnostic systems. Also, the instant results can be streamed to authorized individuals and officials in seconds using the local WiFi/ ethernet systems. This small Business Innovation Research (SBIR) Phase I project is to develop and demonstrate an autonomous and remotely operated millimeter wave (MMW) device that identifies pathogens in seconds in the field. The chemical-free, mobile mini table top systems is to help expand the number of pathogen test stations throughout the USA. This effort utilizes transmitted MMW signals reflected from nanosize pathogens in a manner that captures their unique characteristic via digital signal processing assisted artificial intelligence (AI) structures and stores a reliable unique model of the target pathogen. Utilizing machine learning (ML), the detector autonomously recognizes the pathogen identity within each of the million scans (reflected response packets) of the unidentified samples, each second.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": "1196", "attributes": { "award_id": "2139740", "title": "Evaluating COVID-19 Mitigation Strategies in Schools with a Spatially-Explicit Agent-Based Model of Infection Dynamics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 3057, "first_name": "Lara", "last_name": "Campbell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2023-09-30", "award_amount": 250000, "principal_investigator": { "id": 3058, "first_name": "Ilya", "last_name": "Zaslavsky", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 258, "ror": "", "name": "University of California-San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will develop and refine a simulation modeling system to help schools and school districts evaluate the effects of interventions to control COVID-19 infections among students, focusing first on schools in San Diego County, CA. The goal is to help individual schools choose the most effective strategies for their unique circumstances, particularly each school’s population, classroom layouts and ventilation, neighborhood infection rates, and school bus commutes. The interactive model will allow decision makers at the school and district levels to evaluate the potential impacts of both pharmaceutical interventions, like vaccination and testing, and non-pharmaceutical safety measures including social distancing, mask-wearing, limiting interactions between different student cohorts, decreasing classroom occupancy, and improving ventilation. Finding the best combination of such measures is central to safe school re-opening, which is critical for improving child education and development and allowing parents to return to work. As school-aged children, many of whom are not vaccinated, are disproportionately affected by new and more dangerous COVID-19 variants, this simulation service will provide much-needed insights to our partners among decision-makers at the school and district level in San Diego County and elsewhere in the nation. The school infection model simulates aerosol and droplet transmission as students, teachers, and staff (modeled as human agents) interact during typical school day activities, including classroom instruction, cafeteria lunch, recess, and traveling on a school bus. The likelihood that a healthy agent is exposed to the virus increases as they come near infected, asymptomatic agents or spend significant time in poorly ventilated spaces. The model considers spatial information about room layouts and ventilation, and simulates school day schedules down to 5-minute intervals. Building on a successful prototype for elementary schools, this project will extend the model to middle and high schools and to neighborhoods with different socio-economic and demographic characteristics. Additionally, it will enable simulation of infection dynamics for athletics activities and analysis of infection patterns under different COVID-19 variants. Making the model accessible via a science gateway will let school administrators, researchers, teachers, and students run simulations on a supercomputer, then visualize and compare model outputs under different scenarios. As in the prototype phase, the project will engage undergraduate students in all aspects of model development, and in presenting results to public health and education experts and to the general public – an essential component of their training as data scientists working on societally-impactful topics like education and health equity.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": "1199", "attributes": { "award_id": "2137724", "title": "NSF Convergence Accelerator Track F: How Large-Scale Identification and Intervention Can Empower Professional Fact-Checkers to Improve Democracy and Public Health", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 3067, "first_name": "Mike", "last_name": "Pozmantier", "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": 750000, "principal_investigator": { "id": 3072, "first_name": "Michael W", "last_name": "Wagner", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3068, "first_name": "Porismita", "last_name": "Borah", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3069, "first_name": "Munmun De", "last_name": "Choudhury", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3070, "first_name": "Srijan", "last_name": "Kumar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3071, "first_name": "Sijia", "last_name": "Yang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "Democracy and public health in the United States rely on trust in institutions. Skepticism regarding the integrity of U.S. elections and hesitancy related to COVID-19 vaccines are two consequences of a decline in confidence in basic political processes and core medical institutions. Social media serve as a major source of delegitimizing information about elections and vaccines, with networks of users actively sowing doubts about election integrity and vaccine efficacy, fueling the spread of misinformation. This project seeks to support and empower efforts by journalists, developers, and citizens to fact-check such misinformation. They urgently need tools that can 1) enable testing of fact-checking stories on topics like elections and vaccines as they move across social media platforms like Twitter, Reddit, and Facebook, and 2) deliver feedback on how well the corrections worked in real time and with full performance transparency. Accordingly, this project will develop an interactive system that enables fact-checkers to perform rapid-cycle testing of fact-checking messages and monitor their real-time performance among online communities at-risk of misinformation exposure. To be transparent, all of the underlying code, surveys, and data will be available to share with the social science and computer science communities, and all evidence-based messages of immediate utility to public health professionals and electoral administrators will be made publicly accessible.This project is motivated by a desire to understand and help address two democratic and public health crises facing the U.S.: skepticism regarding the integrity of U.S. elections and hesitancy related to COVID-19 vaccines. Both of these crises are fueled by online misinformation, widely circulating on social media, with networks of users actively sowing doubts about election integrity and vaccine efficacy. The project will deliver an innovative, three-step method to identify, test, and correct real-world instances of these forms of online misinformation. First, using computational means, such as techniques in natural language processing, machine learning, social network analysis and modeling, and computer vision to identify posts and accounts circulating and susceptible to misinformation. Second, lab-tested corrections to the most prominent forms of misinforming claims using recommender systems to optimize message efficacy will be produced. And third, the project will disseminate and evaluate the effectiveness of evidence-based corrections using various scalable intervention techniques available through the platforms sponsored content systems. More specifically, for the first step of the method, the project will use multimodal signal detection and knowledge graph to engage in knowledge driven information extraction about electoral skepticism and vaccine hesitancy on social media, integrating user attributes, message features, and online network structural properties to predict likely exposure to future misinformation and identify susceptible online communities for intervention. The second step will consist of working with professional fact-checking organizations to lab test two types of intervention messages—pre-exposure inoculation and post-exposure correction— aimed at mitigating electoral skepticism and vaccine hesitancy, optimizing them using recommender system techniques. For the third step, field experiments will be conducted that deploy the lab-developed interventions, delivered through a combination of ad-purchasing, automated bots, and online influencers; and assess the success of our interventions with respect to optimal decision-making in both health and democracy-related arenas. Ultimately, this three-step approach can be applied across a range of topics in politics and health.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": "1200", "attributes": { "award_id": "2126610", "title": "SBIR Phase II: An adjuvant-based antimicrobial coating (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 3073, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-15", "end_date": "2023-11-30", "award_amount": 1012000, "principal_investigator": { "id": 3074, "first_name": "Mingyu", "last_name": "Qiao", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 544, "ror": "", "name": "HALOMINE INC.", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 544, "ror": "", "name": "HALOMINE INC.", "address": "", "city": "", "state": "NY", "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). The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to advance a technology that can provide residual antimicrobial activity against viruses, bacteria, and fungi. The COVID-19 pandemic has heightened concerns about microorganisms of all kinds and the limitations of current disinfecting products. The proposed project advances translation of a spray-on technology that, when dried, leaves a thin transparent film sticking both to the surface and to chlorine, thus enabling the surface to be disinfected for up to a month. The film essentially extends the useful life of chlorine-based disinfectants. The proposed project will continue development of a novel antimicrobial coating that has proven efficacy against viruses, bacteria, and fungi. This project will optimize the product formulation for the film and a complementary solution with reproducible residual sanitation performance. The project will further advance the manufacturing scale-up to ensure large volume cost-effective production capabilities for the product and conduct field trials.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": "1203", "attributes": { "award_id": "2134862", "title": "D: Computing the Biome", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 3081, "first_name": "Mike", "last_name": "Pozmantier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2023-09-30", "award_amount": 2583770, "principal_investigator": { "id": 3086, "first_name": "Janos", "last_name": "Sztipanovits", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3082, "first_name": "James M", "last_name": "Pipas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3083, "first_name": "Douglas E", "last_name": "Norris", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3084, "first_name": "David L", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3085, "first_name": "Ethan K", "last_name": "Jackson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Individuals, industries, societies, and governments want to stay healthy. They need cost-effective systems to detect biological threats and predict future disease outbreaks as early as possible. COVID-19 acutely and painfully demonstrated the impacts of the unpredicted. The goals of this program, Computing the Biome, are twofold: (1) demonstrate an extensible data and AI platform that continuously monitors and predicts biothreats in a major U.S. city, and (2) create a framework for economic sustainability and global scalability of these results, by empowering businesses and advanced science missions to consume predictions and produce valuable consumer apps and breakthroughs.This team will produce and interconnect novel data streams ranging from kilometer-scale hyper-local weather, to autonomously identified disease transmitting insects (only millimeters in size), to genomically recognized known and novel viruses (only nanometers in size) – demonstrating that cross-cutting continuous data streams for biothreat detection and prediction can be rapidly unlocked. By combining their expertise in ecology, epidemiology, and virology, the team will design new predictive models and anomaly detectors. This project will develop the first of these high-impact AIs focused on predicting mosquito-borne diseases, which are difficult to control and impact over 600 million people per year. More broadly, the resulting data platform will empower development of new foundational methods for use by the AI community – based on real-world data and grounded in the societal challenges of our age.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": 1384, "pages": 1442, "count": 14420 } } }