Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=3&sort=principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=4&sort=principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=principal_investigator" }, "data": [ { "type": "Grant", "id": "336", "attributes": { "award_id": "2145479", "title": "CAREER: Discourse Processing and Content Generation for Document Simplification", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 595, "first_name": "Tatiana", "last_name": "Korelsky", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2027-08-31", "award_amount": 110797, "principal_investigator": { "id": 596, "first_name": "Junyi", "last_name": "Li", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Simplification is the process of making a text more accessible to a target audience, e.g., language learners, children, and individuals with language impairments, while preserving its meaning and content. The lack of accessible material can exacerbate social issues, for example, the complexity of language used in college admission and financial aid applications has contributed to the lagging access to higher education among emergent bilingual students; the WHO has recognized the urgency of accessible technical information, given the rise of medical misinformation especially in the wake of the COVID-19 pandemic. While there has been much work on sentence simplification, very few datasets are large enough to train supervised models; simplifying a document also involves different operations from those at the sentence level, including content addition, and how sentences connect with each other. This project aims to develop new resources and data-driven approaches for document simplification, with the potential to address information transparency and fair access across a range of high-stake domains. This project will also support the education and training of a diverse body of undergraduate and graduate students across disciplines.To substantially advance document simplification, this CAREER project will tackle several key issues in existing simplification work, including corpora diversity, explanation generation, and document-level approaches. This is achieved by the following research activities: (1) introducing new corpora that tackle the pressing challenge of data diversity in simplification research and enable new application scenarios, especially in the accessibility of technical and jargon-laden texts; (2) tackling content addition and elaboration during simplification---a previously little-explored challenge, and propose a novel, linguistically-informed framework that characterizes and generates elaborations; (3) develop models for document simplification that are informed by structures of discourse, using both coherence structure and entity salience. The innovative ways to integrate discourse target a larger challenge for models to take stretches of discourse into account.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": "337", "attributes": { "award_id": "2219618", "title": "RAPID: #COVID-19: Understanding Community Response in the Emergence and Spread of Novel Coronavirus through Health Risk Communications in Socio-Technical Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 597, "first_name": "Amarda", "last_name": "Shehu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2023-04-30", "award_amount": 908, "principal_investigator": { "id": 598, "first_name": "Arif Mohaimin", "last_name": "Sadri", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 157, "ror": "", "name": "University of Oklahoma Norman Campus", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 157, "ror": "", "name": "University of Oklahoma Norman Campus", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true }, "abstract": "Risk perception and risk averting behaviors of vulnerable communities in the emergence and spread of COVID-19 are spatio-temporal functions of individual or group interactions with their online social neighbors within or outside their communities and such interactions need to be captured through diverse information channels (e.g. traditional outlets such as radio, television, internet and/or non-traditional outlets such as social media). The primary goal of this Rapid Response Research (RAPID) project is to collect time-sensitive online social media and crowd-sourced data and analyze patterns of health-risk communication and community response in the emergence and spread of novel Coronavirus using data-driven methods and network science theories. The major focus will be towards understanding how individuals are socially influenced online, while communicating risk and interacting in their respective communities as the disease continues to spread. The notion of influence will be captured by quantifying the network effects on such communication behavior and characterizing how information is exchanged among people who are socially connected online and exposed to health risk in such outbreaks of disease. Given that communities responded to COVID-19 with limited or no preparation and there is uncertainty in the length of recovery for the communities already affected while new communities being threatened, the data collection effort requires rapid response for better coverage and careful monitoring. The data will include large-scale ephemeral online interactions of people in the affected communities and public officials who are involved in COVID-19 response, recovery, and mitigation efforts, followed by a data-driven network analytics and infographics of COVID-19 risk communication strategies and risk averting behaviors adopted. The proposed research will not only expand the knowledge base of spatio-temporal dynamics of risk perception and dissemination strategies in the emergence and aftermath of a major disease outbreak, but will also result in data-driven inference techniques to improve our understanding of how people express diverse concerns and how to harness and embed such information for designing intervention measures. The methodologies and findings of this rapid response research will benefit emergency management and public health agencies to define targeted information dissemination policies for public with diverse needs based on how people reacted to COVID-19 and their social network characteristics, activities, and interactions in response to similar public health hazards.Public engagement in risk communication can lead to more effective decision-making and enhanced public feedback to the regulatory process. The primary goal of this RAPID project is to mine and analyze large-scale time-sensitive perishable crowd-sourced and social media data (rich spatio-temporal data) and reveal patterns of health-risk communication and community response in the emergence and spread of novel Coronavirus using data-driven methods and network science theories. The specific aims are threefold: (1) to document how public interact and communicate health risk information through their online social networks during a major disease outbreak; (2) to authenticate data from multiple sources and detect anomalies to avoid information overload and spread of misinformation; and (3) to examine how online social networks influence protective actions (e.g., social distancing, self-quarantine decisions) i.e. information cascades in health risk communication. To achieve the goal and aims, the project will utilize ephemeral time and geo-tagged social media interactions of users, agencies, news sources supplemented with crowd-sourced information on COVID-19. This study will have five theoretical and methodological contributions to the literature. It will: (1) advance our understanding of how individuals are socially influenced online, while communicating health risks and interacting in their respective communities as the disease continues to spread; (2) inform the literature on how information is exchanged among people who are socially connected online and exposed to health risk in such outbreaks of disease; (3) use novel machine-learning and network science models to quantify influence and network effects on such communication behavior; (4) capture the variability in network composition, risk communication strategies and risk averting behaviors adopted based on spatio-temporal correlations of risk and disease contagion; (5) ensure authenticity of the collected data from multiple sources and develop more accurate fully-distributed computational algorithms tailored to health risk anomaly detection in socio-technical systems. The findings from this research will be useful to public health and emergency management agencies for tailoring effective information dissemination policies for diverse user groups based on their social network characteristics, activities, and interactions in response to similar public health hazards. The methodologies, and implications of this research can be transferred in designing effective intervention policies to other natural and man-made disaster contexts in which public health risks become major concerns. The project will engage, mentor, and offer an innovative active learning environment for K-12, undergraduate, and graduate students by giving priority to disadvantaged and underrepresented communities in USA. The project will train students on computational skills required for collecting, storing, processing, analyzing and modeling large-scale data using high performance computational resources.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": "8254", "attributes": { "award_id": "1R01AI160780-01", "title": "Human mobility models to forecast disease dynamics and the effectiveness of public health interventions", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 12912, "first_name": "Misrak", "last_name": "Gezmu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-09", "end_date": "2026-03-31", "award_amount": 706315, "principal_investigator": { "id": 601, "first_name": "Derek A", "last_name": "Cummings", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 24072, "first_name": "Amy", "last_name": "Wesolowski", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "Human mobility underlies infectious disease transmission and determines the spatial-temporal dynamics of outbreaks and endemic disease dynamics. Yet, we do not understand how best to incorporate individual or population mobility patterns into models of infectious diseases. Human travel has been successfully incorporated into models used for planning, surveillance, and reactive responses to influenza pandemics, the COVID-19 pandemic, malaria, and others. However, little validation or comparison of approaches used in these models has been performed. Further, there has been no systematic investigation of the extent to which the many different existing sources of human travel data quantify travel patterns, or which descriptions of human mobility are most relevant to disease processes. The small amount of human mobility data available globally requires generalization or extrapolation of features of one dataset to another setting, time or circumstance. This generalization may work for some features of pathogens for a subset of pathogens or transmission routes but may fail miserably in others. It is unlikely that all travel patterns are relevant for all types of diseases. The life history of each pathogen, transmission routes, age structure of incidence and outbreak context will all dictate the importance of specific types of movement. For mobility data to be useful in planning for outbreaks and monitoring interventions, transmission models utilizing mobility data and models must be confronted with epidemiological data (including contact tracing, traditional surveillance, and genetic data) from a variety of sources. Here, we propose to perform the first systematic analysis of existing mobility data and models to identify which models perform best under multiple assumptions using a range of simulations and data from historic outbreaks. We will also identify circumstances when generalized models or non-local data are misleading. To do this, we will collate and standardize a large number of mobility datasets collected by various methods. We will statistically characterize these datasets to identify sources of variation in human mobility at individual, household, community, and larger scales. We will develop multiple candidate models describing mobility and incorporate these candidate models into a range of commonly used models of infectious disease transmission. Proceeding with the principle that human mobility is only useful to models of infectious diseases if it improves our ability to recapitulate the dynamics of observed outbreaks, we will test the ability of each of these candidate mobility models to explain observed patterns of contacts and sequenced pathogens observed in outbreaks of dengue, Zika, Ebola, and COVID-19. In doing this, we will identify conditions under which human mobility can improve our understanding of the transmission and pathogens, inform response strategies and create a resource that can inform responses to multiple current and future outbreaks.", "keywords": [ "2019-nCoV", "Africa", "Age", "COVID-19", "COVID-19 pandemic", "Communicable Diseases", "Communities", "Contact Tracing", "Country", "Data", "Data Collection", "Data Set", "Data Sources", "Dengue", "Dimensions", "Disease", "Disease Outbreaks", "Ebola", "Effectiveness", "Endemic Diseases", "Epidemic", "Epidemiology", "Foundations", "Frequencies", "Future", "Genetic", "Health Policy", "Household", "Human", "Incidence", "Income", "Individual", "Infection", "Intervention", "Investigation", "Malaria", "Mathematics", "Methodology", "Methods", "Modeling", "Molecular", "Monitor", "Movement", "Pathway interactions", "Pattern", "Population", "Population Study", "Process", "Property", "Public Health", "Research", "Research Project Grants", "Resources", "Route", "Rural", "Source", "Standardization", "Statistical Methods", "Structure", "Testing", "Thailand", "Time", "Travel", "Validation", "Variant", "Work", "ZIKA", "base", "data modeling", "disease transmission", "epidemiologic data", "flexibility", "human model", "improved", "individual variation", "infectious disease model", "life history", "low and middle-income countries", "multiple datasets", "novel", "pandemic influenza", "pathogen", "performance tests", "public health intervention", "response", "simulation", "sociodemographics", "transmission process", "urban area" ], "approved": true } }, { "type": "Grant", "id": "8253", "attributes": { "award_id": "5R01AI160780-02", "title": "Human mobility models to forecast disease dynamics and the effectiveness of public health interventions", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 12912, "first_name": "Misrak", "last_name": "Gezmu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-09", "end_date": "2026-03-31", "award_amount": 645548, "principal_investigator": { "id": 601, "first_name": "Derek A", "last_name": "Cummings", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 24072, "first_name": "Amy", "last_name": "Wesolowski", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "Human mobility underlies infectious disease transmission and determines the spatial-temporal dynamics of outbreaks and endemic disease dynamics. Yet, we do not understand how best to incorporate individual or population mobility patterns into models of infectious diseases. Human travel has been successfully incorporated into models used for planning, surveillance, and reactive responses to influenza pandemics, the COVID-19 pandemic, malaria, and others. However, little validation or comparison of approaches used in these models has been performed. Further, there has been no systematic investigation of the extent to which the many different existing sources of human travel data quantify travel patterns, or which descriptions of human mobility are most relevant to disease processes. The small amount of human mobility data available globally requires generalization or extrapolation of features of one dataset to another setting, time or circumstance. This generalization may work for some features of pathogens for a subset of pathogens or transmission routes but may fail miserably in others. It is unlikely that all travel patterns are relevant for all types of diseases. The life history of each pathogen, transmission routes, age structure of incidence and outbreak context will all dictate the importance of specific types of movement. For mobility data to be useful in planning for outbreaks and monitoring interventions, transmission models utilizing mobility data and models must be confronted with epidemiological data (including contact tracing, traditional surveillance, and genetic data) from a variety of sources. Here, we propose to perform the first systematic analysis of existing mobility data and models to identify which models perform best under multiple assumptions using a range of simulations and data from historic outbreaks. We will also identify circumstances when generalized models or non-local data are misleading. To do this, we will collate and standardize a large number of mobility datasets collected by various methods. We will statistically characterize these datasets to identify sources of variation in human mobility at individual, household, community, and larger scales. We will develop multiple candidate models describing mobility and incorporate these candidate models into a range of commonly used models of infectious disease transmission. Proceeding with the principle that human mobility is only useful to models of infectious diseases if it improves our ability to recapitulate the dynamics of observed outbreaks, we will test the ability of each of these candidate mobility models to explain observed patterns of contacts and sequenced pathogens observed in outbreaks of dengue, Zika, Ebola, and COVID-19. In doing this, we will identify conditions under which human mobility can improve our understanding of the transmission and pathogens, inform response strategies and create a resource that can inform responses to multiple current and future outbreaks.", "keywords": [ "2019-nCoV", "Africa", "Age", "COVID-19", "COVID-19 pandemic", "Communicable Diseases", "Communities", "Contact Tracing", "Country", "Data", "Data Collection", "Data Set", "Data Sources", "Dengue", "Dimensions", "Disease", "Disease Outbreaks", "Ebola", "Effectiveness", "Endemic Diseases", "Epidemic", "Epidemiology", "Foundations", "Frequencies", "Future", "Genetic", "Health Policy", "Household", "Human", "Incidence", "Income", "Individual", "Infection", "Intervention", "Investigation", "Malaria", "Mathematics", "Methodology", "Methods", "Modeling", "Molecular", "Monitor", "Movement", "Pathway interactions", "Pattern", "Population", "Population Study", "Process", "Property", "Public Health", "Research", "Research Project Grants", "Resources", "Route", "Rural", "Source", "Standardization", "Statistical Methods", "Structure", "Testing", "Thailand", "Time", "Travel", "Validation", "Variant", "Work", "ZIKA", "base", "data modeling", "disease transmission", "epidemiologic data", "flexibility", "human model", "improved", "individual variation", "infectious disease model", "life history", "low and middle-income countries", "multiple datasets", "novel", "pandemic influenza", "pathogen", "performance tests", "public health intervention", "response", "simulation", "sociodemographics", "transmission process", "urban area" ], "approved": true } }, { "type": "Grant", "id": "338", "attributes": { "award_id": "2223843", "title": "RAPID: Statistical inference of incidence of SARS-CoV-2 in the US using multiple data streams to identify levels of immunity and the impact of non-pharmaceutical interventions", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 599, "first_name": "Samuel", "last_name": "Scheiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2023-03-31", "award_amount": 200000, "principal_investigator": { "id": 601, "first_name": "Derek A", "last_name": "Cummings", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 600, "first_name": "Matthew D", "last_name": "Hitchings", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "The goal of this study is to integrate multiple, independent data sources to estimate the rate of SARS-CoV-2 infections across the US over time. Population-based SARS-CoV-2 serological assays are critical for understanding cumulative incidence and population-level immunity. The US CDC, in partnership with a number of laboratories, has conducted nationwide serosurveys which can help retrospectively assess the cumulative number of total infections. However, data from these surveys may be difficult to interpret due to heterogeneity in antibody response across individuals, by assay, and over time since infection. Reconciling patterns observed in seroprevalence with other data sources including reported COVID-19 cases and deaths can explain variation in seroprevalence across space and time in the US CDC. In addition, the project will estimate the proportion of the population with recent immunizing events (infection or vaccination) to understand the immunity landscape prior to the Omicron-variant-driven wave in 2021-2022 in the US. The project will develop tools to jointly analyze serology, caseand death data, and contribute to the training of a post-doctoral scholar.The primary objective in this study is to integrate multiple independent data streams using statistical and mechanistic models to estimate the rate of seroreversion in assays used in serosurveys across the US, and estimate seroprevalence and cumulative incidence over time by state. The model will provide information about SARS-CoV-2 transmission from case, hospitalization and death data by taking a multi-objective approach and adapting fast inference techniques that we have developed. Methods such as these have been applied to state-leveldata on COVID-19 incidence, including by this group. This project was funded in collaboration with the CDC to support rapid-response research projects to further advance federal infectious disease modeling capabilities.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": "339", "attributes": { "award_id": "2222940", "title": "I-Corps: Comprehensive tool to capture spatio-temporal variations in social media health risk communication for COVID-19 and other health risks", "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": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": "2023-06-30", "award_amount": 16014, "principal_investigator": { "id": 603, "first_name": "Arif Mohaimin", "last_name": "Sadri", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 157, "ror": "", "name": "University of Oklahoma Norman Campus", "address": "", "city": "", "state": "OK", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of a software platform that may be integrated into crisis management systems such as public health (WHO, CDC), emergency management (FEMA), and transportation (DOT) agencies to facilitate the transmission of correct information and provide the option to notify social media providers of identified misinformation. It is becoming increasingly important for government agencies, policy makers, and emergency management officials to be capable of addressing major crisis scenarios under acute time and resource constraints. Using social media platforms more efficiently would be a critical step towards this vision. For example, such communications platforms could to be leveraged to better communicate the COVID-19 risk. The goal of this project is to understand and validate the need for this capability in civilian or emergency management agencies, and federal, state, or city level government agencies. The proposed technology also may be useful in other natural and man-made disaster contexts in which public health risks become major concerns.This I-Corps project is based on the development of a comprehensive tool to capture spatio-temporal variations in social media health risk communication (i.e., information or misinformation) at different scales. The project will also integrate data-driven methods for user-friendly predictive analytics and infographics to anticipate citizen needs and crisis responses. The proposed tool will be grounded on state-of-the-art network science, social science, and data science theories and concepts. Using the Application Programming Interface (API) of publicly available social media platforms such as Twitter, large-scale crisis communication data has been collected in the emergence and outbreak of the novel coronavirus. These data may serve as proof-of-concept for the ability to develop and operate publicly-available, novel social sharing platforms to automatically and passively detect and control information tipping points to facilitate better response in pandemics and other societal emergencies. As such, the proposed approach will provide holistic support to detect information overload, turnover, user reaction, and response in socio-technical systems during a major crisis.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": "9579", "attributes": { "award_id": "2229218", "title": "I-Corps: Stoma-Wafer Care Management Tool Using Mobile Phone Technology", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 722, "first_name": "Lydia", "last_name": "McClure", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2023-01-31", "award_amount": 50000, "principal_investigator": { "id": 607, "first_name": "Rania", "last_name": "Hussein", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of a remote stoma monitoring and management technology to encourage telehealth for routine wound, ostomy, and continence (WOC) care consultations in order to reduce the burden on nurses, keep patients and the nurses safe from respiratory infections like COVID-19, and reduce the use of painful personal protective equipment (PPE) during routine checkups. This product may be adapted to wound care applications and used to monitor clinical wound healings with the use of 3D imaging. The product seeks to improve the precision of wafer fit and overall ease of stoma management while simplifying the appliance-fit process and empowering patients with easy-to-use tools to manage their stomas. The mobile technology could be used as a scanning device as well as a healthcare package to support individuals that have undergone an ostomy.\n\nThis I-Corps project is based on the development of a software-based stoma-wafer inspection and fit tool that improves the precision of wafer fit and the overall ease of stoma management. In this system, a 3D model of the stomal area will be processed on a cloud-based backend, and an “ideal” aperture contour will be generated and used for automatic, high-precision wafer cutting. The product could be a reference for rethinking medical imaging technology that typically requires expensive image acquisition systems such as computed tomography (CT) and magnetic resonance imaging (MRI). While the initial focus of the product is to provide an affordable stoma care management system for patients, the technology could be applied to a wide range of other wound care products and fasteners.\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": "340", "attributes": { "award_id": "2141798", "title": "A New Generation of Broadly Accessible Remote Engineering Laboratories", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 604, "first_name": "Eric", "last_name": "Sheppard", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2024-03-31", "award_amount": 598388, "principal_investigator": { "id": 607, "first_name": "Rania", "last_name": "Hussein", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 605, "first_name": "Denise", "last_name": "Wilson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 606, "first_name": "Payman", "last_name": "Arabshahi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by establishing a new generation of remote engineering labs that support underserved communities and schools with limited resources. Lessons learned during the COVID-19 crisis have helped educators rethink teaching practices that are sustainable and safe after the pandemic era. Offering hands-on engineering labs in off-campus settings has presented significant challenges to educators. By taking advantage of advances in cloud computing, implementing a remote hardware laboratory will allow students to experience a full-fledged remote experience without compromising what they could have learned and accomplished if they were physically present in the lab. This project will advance the potential of using remote laboratories for electrical and computer engineering students in embedded computing and wireless communications courses. The proposed work is expected to allow educators and institutions to rethink the delivery of hands-on engineering labs via a cost-effective, broadly accessible, and equitable solution. The complete remote lab solution, including hardware and software, has significance to underprivileged universities and K-12 education.The project’s goal is to develop a remote computing and wireless communication laboratory based on field programmable gate arrays (FPGAs) and software defined radio (SDR) platforms; provide a full technical evaluation of remote solutions; and perform a comprehensive assessment of student learning and engagement in remote settings for these engineering technologies. The scope of the work is scalable, and the open-source hardware and software toolkit that will be developed can be deployed at other institutions, as well as K-12 and underserved community settings, to provide access to industry-grade hardware to all students. The sustainability plan includes a scalable solution that allows universities to pool their individual remote labs together to further increase access and decrease equipment costs and foster further collaboration among institutions by sharing resources and pedagogical content. The open-source remote labs will be disseminated via a highly modular repository (GitHub), and partnerships between schools will be encouraged to improve course materials, perform version control, pull requests, provide issue tracking, and use the course materials at their universities. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "341", "attributes": { "award_id": "2148705", "title": "Collaborative Research: Enhancing MPOWIR to Build a Diverse and Inclusive Oceanography Workforce", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 608, "first_name": "Baris", "last_name": "Uz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2026-03-31", "award_amount": 147021, "principal_investigator": { "id": 609, "first_name": "Mona", "last_name": "Behl", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 160, "ror": "", "name": "University of Georgia Research Foundation Inc", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 160, "ror": "", "name": "University of Georgia Research Foundation Inc", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "MPOWIR (Mentoring Physical Oceanography Women to Increase Retention) is a community-led program aimed at providing mentoring to junior women and other marginalized and underrepresented genders (herein referred to women+) in physical oceanography to improve their retention in the field. Since its inception in 2007, MPOWIR has made substantial contributions to decreasing the attrition of women+ physical oceanographers. However, MPOWIR’s work is far from being done. Shifting demographics, the impact of COVID-19 on the careers of women+, and longstanding structural inequities remain challenges to retention. This project would sustain and enhance MPOWIR for another 4 years. It seeks to improve retention through a series of interrelated objectives that include: (1) providing continuity of mentoring from a woman’s+ career transition from graduate school to postdoctoral years to the early years of her permanent job, (2) providing mentorship training to MPOWIR participants, (3) fostering a sense of community in physical oceanography, (4) broadening participation in MPOWIR by providing training and professional development opportunities to all those who self-identify as physical oceanographers, and (5) engaging a cross-section of stakeholders to develop a shared vision for the next decade of MPOWIR. To meet the needs and expectations of its stakeholders, MPOWIR aims to enhance its design by incorporating a few new initiatives to provide additional professional development opportunities and support to the community of mentors and peers that MPOWIR has helped build over the past 16 years, and lead to improvement in the overall culture of the physical oceanography community.Specifically, this funding supports the following MPOWIR activities:1. Pattullo Conference held biannually, brings ~25 junior women+ physical oceanographers together with 12 senior physical oceanographers of all genders for a 2.5 day meeting focused on discipline-based mentoring.2. Mentoring groups of ~6 junior and 2 senior women+ physical oceanographers meet for a monthly teleconference, for the purpose of confidential, personalized mentoring.3. MPOWIR website serves as a repository of resources for mentoring and physical oceanography careers.4. MPOWIR webinars and virtual discussions held semi-annually, focus on topics of particular interest to those in the early stages of a physical oceanographer’s first position, provide continued support for previous participants, expand gender neutral participation, and connect to the broader scientific community.5. Townhalls held at large conferences, such as the Ocean Sciences Meeting, provide a forum for dissemination of information and communication with the whole oceanographic community.6. Databases and surveys are conducted to assess the effectiveness of MPOWIR activities, determine community mentoring needs, and evaluate progress in retention.7. NEW - Mentorship training for mentors and mentees who participate in MPOWIR.8. NEW - Virtual Professional Development Conference held in the intervening years between Pattullo conferences with the aim of broadening participation, expanding training, and networking opportunities.9. NEW - External program evaluation and strategic planning to assess the impact of MPOWIR and establish a shared vision with the oceanographic community for the next decade of the program.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": "342", "attributes": { "award_id": "2148704", "title": "Collaborative Research: Enhancing MPOWIR to Build a Diverse and Inclusive Oceanography Workforce", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 610, "first_name": "Baris", "last_name": "Uz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2026-03-31", "award_amount": 579183, "principal_investigator": { "id": 611, "first_name": "Colleen B", "last_name": "Mouw", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 161, "ror": "https://ror.org/013ckk937", "name": "University of Rhode Island", "address": "", "city": "", "state": "RI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 161, "ror": "https://ror.org/013ckk937", "name": "University of Rhode Island", "address": "", "city": "", "state": "RI", "zip": "", "country": "United States", "approved": true }, "abstract": "MPOWIR (Mentoring Physical Oceanography Women to Increase Retention) is a community-led program aimed at providing mentoring to junior women and other marginalized and underrepresented genders (herein referred to women+) in physical oceanography to improve their retention in the field. Since its inception in 2007, MPOWIR has made substantial contributions to decreasing the attrition of women+ physical oceanographers. However, MPOWIR’s work is far from being done. Shifting demographics, the impact of COVID-19 on the careers of women+, and longstanding structural inequities remain challenges to retention. This project would sustain and enhance MPOWIR for another 4 years. It seeks to improve retention through a series of interrelated objectives that include: (1) providing continuity of mentoring from a woman’s+ career transition from graduate school to postdoctoral years to the early years of her permanent job, (2) providing mentorship training to MPOWIR participants, (3) fostering a sense of community in physical oceanography, (4) broadening participation in MPOWIR by providing training and professional development opportunities to all those who self-identify as physical oceanographers, and (5) engaging a cross-section of stakeholders to develop a shared vision for the next decade of MPOWIR. To meet the needs and expectations of its stakeholders, MPOWIR aims to enhance its design by incorporating a few new initiatives to provide additional professional development opportunities and support to the community of mentors and peers that MPOWIR has helped build over the past 16 years, and lead to improvement in the overall culture of the physical oceanography community.Specifically, this funding supports the following MPOWIR activities:1. Pattullo Conference held biannually, brings ~25 junior women+ physical oceanographers together with 12 senior physical oceanographers of all genders for a 2.5 day meeting focused on discipline-based mentoring.2. Mentoring groups of ~6 junior and 2 senior women+ physical oceanographers meet for a monthly teleconference, for the purpose of confidential, personalized mentoring.3. MPOWIR website serves as a repository of resources for mentoring and physical oceanography careers.4. MPOWIR webinars and virtual discussions held semi-annually, focus on topics of particular interest to those in the early stages of a physical oceanographer’s first position, provide continued support for previous participants, expand gender neutral participation, and connect to the broader scientific community.5. Townhalls held at large conferences, such as the Ocean Sciences Meeting, provide a forum for dissemination of information and communication with the whole oceanographic community.6. Databases and surveys are conducted to assess the effectiveness of MPOWIR activities, determine community mentoring needs, and evaluate progress in retention.7. NEW - Mentorship training for mentors and mentees who participate in MPOWIR.8. NEW - Virtual Professional Development Conference held in the intervening years between Pattullo conferences with the aim of broadening participation, expanding training, and networking opportunities.9. NEW - External program evaluation and strategic planning to assess the impact of MPOWIR and establish a shared vision with the oceanographic community for the next decade of the program.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": 3, "pages": 1392, "count": 13920 } } }