Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=-awardee_organization
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-awardee_organization", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-awardee_organization", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-awardee_organization", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=-awardee_organization" }, "data": [ { "type": "Grant", "id": "1569", "attributes": { "award_id": "2027387", "title": "RAPID: Rumor Diffusion During Unrest", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "7914", "9179" ], "program_officials": [ { "id": 4099, "first_name": "Melanie", "last_name": "Hughes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-01", "end_date": "2022-09-30", "award_amount": 74000, "principal_investigator": { "id": 4100, "first_name": "Kyounghee", "last_name": "Kwon", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project examines diffusion of rumors and misinformation during unrest. The context is a large city where the COVID-19 outbreak has happened amid a large-scale collective action. By empirically examining how falsehoods feed and are fed by collective behaviors in this situation, the project aims to understand how misinformation and rumors both online and offline co-evolve during a period of unrest. Understanding rumor diffusion during unrest contributes to identifying challenges for consensus building in contemporary communication ecology. By studying rumor diffusion in a large-scale context, the study adds value in knowing how authorities use misinformation. The project will have impact on training of future practitioners in terms of how to deal with news about unrest. Research questions concern variation in rumors, misinformation, and the sharing of these; interpolation of COVID-19 rumors into collective action narratives; differences in the patterns of rumors and rumor-debunking messages; and the association between beliefs in misinformation and participation in collective action. Two methodological approaches are taken. First, string-matching techniques are employed to identify rumors and rumor-debunking messages from a large corpus of digital data, crawled from social media platforms. Computational methods including structural topic modeling and diffusion tree network analysis are used to infer coherent themes across rumor messages and to examine rumor diffusion patterns in terms of depth, width, and interlayer ratios. Second, online surveys are conducted in both regions using a stratified sample of about 1,500 anonymous participants. Regression modeling is performed to understand relationships among beliefs in different types of rumors, institutional trust, and protest support.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": "1778", "attributes": { "award_id": "2031799", "title": "RAPID: SaTC: FACT: Federated Analytics based Contact Tracing for COVID-19", "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": [ "025Z", "065Z", "096Z", "7914", "9102" ], "program_officials": [ { "id": 4682, "first_name": "James", "last_name": "Joshi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2023-05-31", "award_amount": 208000, "principal_investigator": { "id": 4685, "first_name": "Lalitha", "last_name": "Sankar", "orcid": "https://orcid.org/0000-0001-8122-5444", "emails": "[email protected]", "private_emails": "", "keywords": "['Statistical Learning Theory with focus on Loss Functions for learning']", "approved": true, "websites": "['https://sankar.engineering.asu.edu/', 'https://sankar.engineering.asu.edu/fact-federated-analytics-based-contact-traci…']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4683, "first_name": "Ming", "last_name": "Zhao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4684, "first_name": "Ni", "last_name": "Trieu", "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": "The spread of the Corona Virus Disease 2019 (COVID-19), a highly-infectious disease caused by a newly discovered coronavirus, has reached pandemic levels across the globe. As the numbers in the USA of infections, critical care interventions and deaths continue to rise, mobile applications (apps) that enable contact tracing (CT) are being rapidly deployed to monitor the spread of COVID-19. However, on-going deployments, based on anonymously sharing tokens without exploiting the rich local device data, are insufficient in monitoring disease spread in a timely manner and are also vulnerable to privacy and security attacks. There is an urgent need to develop CT apps that not only monitor but also intervene to limit COVID-19 spread while respecting user security and privacy. This project addresses this challenge via Federated Analytics based Contact Tracing (FACT), a refined federated learning approach to leverage both device-level data and server capabilities in a private and secure manner. FACT enables prevention and intervention by including hotspot identification, user alerts, and continual assessment of user COVID-19 risk. FACT guarantees a private and secure way to (i) evaluate a user's need for testing or their resilience to exposure, and (ii) assess herd immunity across the population.FACT addresses both the vulnerability of current Bluetooth-based systems to a variety of attacks and limited learning at the server by proposing a secure GPS+Bluetooth system which will enable the server to detect geographical infection clusters in a privacy-preserving manner. FACT also harnesses the rich device-level mobility and acoustic sensing data to periodically predict risks of those exposed to COVID-19 positive patients using federated learning and without sharing any device data with the server. Several simple, but well-validated, parameters are extracted from these sensors to develop local digital markers of COVID risk. At the heart of these innovations are the refinements FACT brings to standard federated learning via knowledge distillation and model compression. This project, with the support of ASU University Technology Office and collaboration with industry companies, will deploy and evaluate FACT via a mobile app and reach many users. FACT can extend the clinical utility of acoustic measures, adopted in general clinical trials, for use in COVID-19 patients. This research also provides immense opportunities to train and expose diverse graduate students to the technical challenges of ensuring privacy and security while simultaneously enabling socially beneficial technologies.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": "1796", "attributes": { "award_id": "2038372", "title": "RAPID: Tribal capacity to evaluate COVID-19 using wastewater-based epidemiology", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4739, "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": "2020-07-15", "end_date": "2021-07-31", "award_amount": 200000, "principal_investigator": { "id": 4742, "first_name": "Otakuye", "last_name": "Conroy-Ben", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4740, "first_name": "Rebecca", "last_name": "Muenich", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4741, "first_name": "Erin M", "last_name": "Driver", "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": "The COVID-19 pandemic has greatly impacted society. These impacts are particularly hard on Native American communities due to healthcare disparities, multi-generational housing, and limitations in sanitation infrastructure. As a result, mandatory curfews, border closures, and shutdowns have been implemented to prevent further spread of COVID-19 in Tribal communities. However, it is difficult to assess the success of such measures without routine testing. The goal of this project is to determine whether wastewater-based epidemiology can be used to monitor SARS-CoV-2 in Tribal communities to rapidly assess COVID-19 outbreaks. Results from this assessment will help Tribal health care professionals assess the impact of quarantine and lock-down strategies. Training and outreach materials will be developed to help Tribal leaders protect the health and welfare of their communities both during the current pandemic, and in future outbreaks.The COVID-19 pandemic has impacted Native American communities particularly hard due to the prevalence of communal living, lack of potable water infrastructure, limited access to internet and cable services which restricts the flow of public health information, and health disparities that affect infection rates and outbreaks. Tribal leadership across the country has implemented strict mandatory curfew and lock-down ordinances, border closures, and government shutdowns to slow the spread of the pandemic. However, routine testing of COVID-19 is not feasible in these resource-limited communities, making it difficult to assess the effectiveness of control measures. The goal of this project is to investigate the feasibility of using wastewater-based epidemiology (WBE) to monitor SARS-CoV-2 (the virus that causes COVID-19) in Tribal communities. This goal will be achieved through an evaluation of wastewater permit data to target Tribal communities where SARS-CoV-2 will be measured in wastewater treatment systems. Results will be used to develop culturally appropriate WBE research training and educational materials for wastewater utility operators, health professionals, and Tribal leaders. Successful implementation of WBE would enable Tribal communities to rapidly evaluate community health and assess the success of intervention measures.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": "1824", "attributes": { "award_id": "2026860", "title": "Collaborative Research: RAPID: RTEM: Rapid Testing as Multi-fidelity Data Collection for Epidemic Modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "1638", "7914", "8084", "9263" ], "program_officials": [ { "id": 4816, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-15", "end_date": "2022-03-31", "award_amount": 122998, "principal_investigator": { "id": 4819, "first_name": "Giulia", "last_name": "Pedrielli", "orcid": "https://orcid.org/0000-0001-6726-9790", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://www.rtem.live/']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4817, "first_name": "K. Selcuk", "last_name": "Candan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4818, "first_name": "Maria Luisa", "last_name": "Sapino", "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": "The novel coronavirus (COVID-19) epidemic is generating significant social, economic, and health impacts and has highlighted the importance of real-time analysis of the spatio-temporal dynamics of emerging infectious diseases. COVID-19, which emerged out of the city of Wuhan in China in December 2019 is now spreading in multiple countries. It is particularly concerning that the case fatality rate appears to be higher for the novel coronavirus than for seasonal influenza, and especially so for older populations and those with prior health conditions such as cardiovascular disease and diabetes. Any plan for stopping the epidemic must be based on a quantitative understanding of the proportion of the at-risk population that needs to be protected by effective control measures in order for transmission to decline sufficiently and quickly enough for the epidemic to end. Different data collection and testing modalities and strategies available to help calibrate transmission models and predict the spread/severity of a disease, have variable costs, response times, and accuracies. In this Rapid Response Research (RAPID) project, the team will examine the problem of establishing optimal practices for rapid testing for the novel coronavirus. The result will be the Rapid Testing for Epidemic Modeling (RTEM), which will translate into science-based predictions of the COVID-19 epidemic's characteristics, including the duration and overall size, and help the global efforts to combat the disease. The RTEM will fill an important gap in data-driven decision making during the COVID-19 epidemic and, thus, will enable services with significant national economic and health impact. The educational impact of the project will be on mentoring of post-doctoral and PhD researchers and on curricula by incorporating research challenges and outcomes into existing undergraduate and graduate classes. Computational models for the spatio-temporal dynamics of emerging infectious diseases and data- and model-driven computer simulations for disease spreading are increasingly critical in predicting geo-temporal evolution of epidemics as well as designing, activating, and adapting practices for controlling epidemics. In this project, the researchers tackle a Rapid Testing for Epidemic Modeling (RTEM) problem: Given a partially known target disease model and a set of testing modalities (from surveys to surveillance testing at known disease hotspots), with varying costs, accuracies, and observational delays, what is the best rapid testing strategy that would help recover the underlying disease model? Several scientific questions arise: What is the value of testing? Should only sick people be tested for virus detection? What level of resources should be devoted to the development of highly accurate tests (low false positives, low false negatives)? Is it better to use only one type of test aiming at the best cost/effectiveness trade off, or a non-homogeneous testing policy? Naturally these questions need to be investigated at the interface of epidemiology, computer science, machine learning, mathematical modeling and statistics. As part of the work, the team will develop a model of transmission dynamics and control, tailored to COVID-19 in a way that accommodates diagnostic testing with varying fidelities and delays underlying a rapid testing regimen. The investigators will further integrate the resulting RTEM-SEIR model with EpiDMS and DataStorm for executing continuous coupled simulations.This project is jointly funded through the Ecology and Evolution of Infectious Diseases program (Division of Environmental Biology) and the Civil, Mechanical and Manufacturing Innovation program (Engineering).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": "1832", "attributes": { "award_id": "2032114", "title": "RAPID: Collaborative Research: Covid-19 Hotspot Network Size and Node Counting using Consensus Estimation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "153E", "7914" ], "program_officials": [ { "id": 4837, "first_name": "Zhengda", "last_name": "Zhengdao", "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": 107995, "principal_investigator": { "id": 4839, "first_name": "Andreas S", "last_name": "Spanias", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4838, "first_name": "Cihan", "last_name": "Tepedelenlioglu", "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": "In order to open up the economy in light of the reality of COVID-19, a suite of solutions are needed to minimize the spread of COVID-19 which include providing tools for businesses to minimize the risk for their employees and customers. It is important to detect transmission hotspots where the contact between infected and uninfected persons is higher than average. This project will provide information to assess precisely the size, density and locations of COVID-19 hotspots and enable issuing well-informed advisories based on data-driven continuous risk assessment. Every step will be taken to ensure privacy and network security and specific algorithms will be developed for secure access and information transfer. The project will access databases at CDC, Johns Hopkins and the WHO, and create a comprehensive website to disseminate real-time localized COVID-19 hotspot data, while maintaining privacy. The project will create new algorithms and embed them in iOS and Android apps that will continuously interact with databases. The software for mobile devices as well as central hubs will be made publicly available through APIs for use by the broader community.The project will use advanced consensus-based methods for estimating network area/size, node locations and node counts in a network based on minimal transmit-receive data. The proposed methods will lead to significant improvements compared to existing algorithms. The project will design consensus-based algorithms to estimate (a) the center, radius, and consequently, the size of the network, and (b) the number of users in the network. Localization algorithms will be designed that work with noisy and incomplete data. The proposed work is different from the contact-tracing technology used by Google and Apple which is limited to newer devices. The proposed algorithms and software will advance the state of the art while retaining compatibility with emerging and existing mobile technology. The project will help reduce COVID-19 infections and save lives. The research will also have applicability to other fields such as the E911 system, indoor user tracking, infrastructure-free implementations applicable to robotics, autonomous systems and vehicle fleets, and location-aware patient care and other mobile health applications. The developed algorithms can be used in other emergency situations, such as locating clusters of sheltering groups in the case of earthquakes and tsunamis, to assist first responders in finding survivors after an event, and for detection of transmission nodes in the case of future pandemics or future waves of COVID-19. Outreach activities will be integrated with the research and include the creation of software and web content for dissemination.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": "1939", "attributes": { "award_id": "2028564", "title": "RAPID: COVID-19’s Impact on the Urban Environment, Behavior, and Wellbeing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5151, "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": "2020-05-01", "end_date": "2021-04-30", "award_amount": 199998, "principal_investigator": { "id": 5154, "first_name": "Rolf U", "last_name": "Halden", "orcid": "https://orcid.org/0000-0001-5232-7361", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://covid19.tempe.gov/', 'https://biodesign.asu.edu/environmental-health-engineering/human-health-observa…']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5152, "first_name": "Matthew L", "last_name": "Scotch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, { "id": 5153, "first_name": "Arvind", "last_name": "Varsani", "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": "SARS CoV2, the coronavirus responsible for the global severe acute respiratory syndrome pandemic (COVID-19) represents a major threat to the health and welfare of the public. Currently the US death toll is nearing 30,000 and estimates exceeding a hundred thousand fatalities if the spread of the virus continues unchecked. Public health interventions including strict shelter in place rules have been put in place across the US and globally to limit the spread of the virus through communities. This has had a dramatic impact on the economy, with millions of people becoming unemployed in the immediate aftermath of the interventions. The goal of this RAPID project is to develop the science for rapid assessment of how public health interventions in response to the COVID-19 pandemic are impacting the environment, human behavior, and the wellbeing of the public. This goal will be achieved through detailed characterization of various biomarkers of environmental health and wellbeing in wastewater collected in the service area of people living in Tempe, Arizona. The information collected will be available to city officials, other community stakeholders, and the public using an online platform for emergency response. The resources developed in this project will inform the Nation of the impacts of shelter in place and assist crisis management and decision makers in managing these interventions.The goal of this project is to employ detailed high-resolution analysis of wastewater together with geospatial modeling to develop a rapid assessment of environmental health at the community level during the mandated health interventions in response to the global COVID-19 pandemic. The study will take place in Tempe, Arizona, a city of 185,000 people. Baseline data on environmental quality and human behavior and health have been collected by the research team for the past two years leading up to the pandemic. A transdisciplinary team consisting of an environmental engineer, a virologist, and a bioinformatician will study environmental and human health impacts associated with the global pandemic and the public health interventions implemented in the city in response to the outbreak using this unique data resource. Urban wastewater will be sampled daily during the shelter in place intervention for analysis of a broad spectrum of compounds and biomarkers using liquid chromatography tandem mass spectrometry. Analyses include the types and quantities of air pollutants, medications taken as a result of fever and viral infections (e.g., ibuprofen), allergy suppressants, stimulants and depressants (such as nicotine and alcohol), drugs of abuse, dietary markers (indicating potential food shortages), and general biomarkers suggestive of human wellbeing and health status (e.g., antidepressants). US census data will enable an interpretation of study findings using demographics and population-size data relevant for crisis management and evidence-based decision making by scientists, city planners, mayors, health agencies, healthcare providers, and policy makers.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": "1946", "attributes": { "award_id": "2031708", "title": "RAPID: Emotional and neural influences on decision-making in the context of COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "7252", "7914" ], "program_officials": [ { "id": 5170, "first_name": "Michael", "last_name": "Hout", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-05-31", "award_amount": 118726, "principal_investigator": { "id": 5172, "first_name": "Samuel M", "last_name": "McClure", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5171, "first_name": "Gene A Brewer", "last_name": "Jr.", "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": "Emotions color and shape the way people perceive the world and thus how they make decisions and form memories; and those emotional impacts in turn can depend upon specific brain structures and functions. These effects are especially consequential in the context of the emotionally charged decisions and actions needed to respond effectively to the COVID-19 pandemic. To address these issues, in combination with data on pre-pandemic behavior and brain architecture, this project will measure how emotional state during the COVID-19 pandemic alters decision-making, and how that varies with the structure of the brain's dopamine system. Improved understanding of decision making in the context of the pandemic may enable us to design new interventions and information formats that will increase preparedness and resilience. The effects of emotions on decisions and memory have both been posited to depend on the brain’s dopamine system, which is involved in processing emotions and rewards. The project will determine the extent to which dopamine system anatomy and function predict changes in decision-making and memory in the current context of the pandemic. This project leverages a recently completed neuroimaging study that includes structural, functional, and diffusion-weighted data, which characterized the dopamine system in a group of 50 participants who completed a series of cognitive and behavioral tasks assessing impulsivity, inhibitory control, and value-directed memory formation. To assess the emotional impact of the COVID-19 pandemic on decision-making, these same cognitive and behavioral tasks will be made available online to the same group of participants from the previous study. The degree to which emotional state, and pre-existing dopamine system anatomy, predicts differences in decision-making and memory formation relative to baseline will be tested. Dopamine system anatomy is predicted to moderate the relationship among emotions, cognition, and behavior. Overall, the project will contribute to an understanding of how brain and emotion combine to impact behavioral responses to COVID-19.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": "1957", "attributes": { "award_id": "2030013", "title": "RAPID: Winners and Losers when Science Moves Home: Differential Effects of COVID-19 based on Discipline, Caregiving, and Career Stage", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5210, "first_name": "Georgia", "last_name": "Kosmopoulou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2022-07-31", "award_amount": 175687, "principal_investigator": { "id": 5214, "first_name": "Monica", "last_name": "Gaughan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5211, "first_name": "Stephanie L", "last_name": "Pfirman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5212, "first_name": "Barry", "last_name": "Bozeman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5213, "first_name": "Miki", "last_name": "Kittilson", "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": "COVID-19 is having widespread—but disparate—effects on the nation's scientific capacity housed in the higher education sector, including the STEM researchers who conduct basic and applied research, and who train future generations of scientists. One-half of basic research conducted in the United States is performed in the higher education sector, and the federal government finances over half of it. The research universities, and the academic STEM researchers who are employed by them, are essential to the national science system. The overarching question of this project is, how do changes brought about by COVID-19 affect the productivity of academic STEM researchers and the science they conduct? Results will inform policy making to help the core of the nation's research enterprise to meet the challenge of conducting research vital to the nation’s interests during a worldwide pandemic. Although COVID-19 is a specific instance of a macro-level disruption to science, findings from this study will also inform how to build STEM research capacity to be resilient when confronted with future large-scale changes. Because academic STEM researchers are also student research mentors and educators, understanding adaptations such as use of new technologies, will improve the quality and impact of STEM education and contribute to a better prepared STEM workforce.In this multi-method study, we study the impacts of macro social disruption and dislocation on patterns of academic productivity, choice of research topics and projects pursed, and sustaining or rethinking patterns of collaboration and teamwork. The first arm of the study is to conduct semi-structured qualitative interviews of STEM professors in the following broad areas: large-scale infrastructure-dependent research (e.g., that requiring large congregate specialized research facilities, such as observatories); smaller-scale infrastructure-dependent research (e.g., laboratories located at universities); fieldwork dependent research; applied research; and analytic research. We expect differences in impact on research productivity across these different types: We hypothesize that infrastructure-dependent science will be affected more negatively than other modes of science. In selecting participants in the qualitative arm, we will ensure diversity across a variety of dimensions to test our hypothesis that the COVID-19 crisis will have especially negative impacts on early career researchers, and those with more caretaking responsibilities. Results from the qualitative arm of the study will enable the development of extensive indices of potential impacts and scientific adjustments, which will then form the basis of a national survey to assess the project's hypotheses on a nationally representative survey of academic scientists. The study design is longitudinal, including two waves of the survey.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": "1973", "attributes": { "award_id": "2027529", "title": "Collaborative:RAPID: Leveraging New Data Sources to Analyze the Risk of COVID-19 in Crowded Locations", "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": [ "077Z", "096Z", "7914", "8004" ], "program_officials": [ { "id": 5261, "first_name": "Seung-Jong", "last_name": "Park", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2022-12-31", "award_amount": 49664, "principal_investigator": { "id": 5262, "first_name": "Matthew L", "last_name": "Scotch", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "The goal of this project is to create a software infrastructure that will help scientists investigate the risk of the spread of COVID-19 and analyze future epidemics in crowded locations using real-time public webcam videos and location based services (LBS) data. It is motivated by the observation that COVID-19 clusters often arise at sites involving high densities of people. Current strategies suggest coarse scale interventions to prevent this, such as cancellation of activities, which incur substantial economic and social costs. More detailed fine scaled analysis of the movement and interaction patterns of people at crowded locations can suggest interventions, such as changes to crowd management procedures and the design of built environments, that yield social distance without being as disruptive to human activities and the economy. The field of pedestrian dynamics provides mathematical models that can generate such detailed insight. However, these models need data on human behavior, which varies significantly with context and culture. This project will leverage novel data streams, such as public webcams and location based services, to inform the pedestrian dynamics model. Relevant data, models, and software will be made available to benefit other researchers working in this domain, subject to privacy restrictions. The project team will also perform outreach to decision makers so that the scientific insights yield actionable policies contributing to public health. The net result will be critical scientific insight that can generate a transformative impact on the response to the COVID-19 pandemic, including a possible second wave, so that it protects public health while minimizing adverse effects from the interventions.We will accomplish the above work through the following methods and innovations. LBS data can identify crowded locations at a scale of tens of meters and help screen for potential risk by analyzing the long range movement of individuals there. Worldwide video streams can yield finer-grained details of social closeness and other behavioral patterns desirable for accurate modeling. On the other hand, the videos may not be available for potentially high risk locations, nor can they directly answer “what-if” questions. Videos from contexts similar to the one being modeled will be used to calibrate pedestrian dynamics model parameters, such as walking speeds. Then the trajectories of individual pedestrians will be simulated in the target locations to estimate social closeness. An infection transmission model will be applied to these trajectories to yield estimates of infection spread. This will result in a novel methodology to include diverse real time data into pedestrian dynamics models so that they can quickly and accurately capture human movement patterns in new and evolving situations. The cyberinfrastructure will automatically discover real-time video streams on the Internet and analyze them to determine the pedestrian density, movements, and social distances. The pedestrian dynamics model will be reformulated from the current force-based definition to one that uses pedestrian density and individual speed, both of which can be measured effectively through video analysis. The revised model will be used to produce scientific insight to inform policies, such as steps to mitigate localized outbreaks of COVID-19 and for the systematic reopening, potential re-closing, and permanent changes to economic and social activities.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": "2043", "attributes": { "award_id": "2032513", "title": "RAPID: How social norms impact COVID-19 transmission behaviors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "1390", "7914", "9178", "9179" ], "program_officials": [ { "id": 5483, "first_name": "Jeffrey", "last_name": "Mantz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2021-11-30", "award_amount": 66882, "principal_investigator": { "id": 5485, "first_name": "Sarah", "last_name": "Mathew", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5484, "first_name": "Minhua", "last_name": "Yan", "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": "Until a vaccine is developed, behavior change is the only way to stem the transmission of COVID-19 and safely resume economic activities. Social norms – aggregate behaviors, private preferences, judgments of behaviors, etc. – affect individuals' likelihood of adopting behavior change and must be understood in order to model effectively the impact of behavior-based interventions on disease transmission. This project investigates how community norms and normative pressures, in conjunction with population demographic composition, government policies, and information that people receive, determine behaviors targeted by COVID-19 health interventions. The results of the research will be disseminated quickly and broadly to facilitate efforts to stem disease transmission. The research trains a U.S. based graduate student and undergraduate students. The study evaluates two causal relationships: 1) how government policies and individual traits (e.g., socio-economic position, personality) cause an individual to adopt a behavior (e.g., interpersonal distance, mask wearing); and 2) how aggregate patterns at the community level cause differences in transmission patterns. Surveys will be administered in five populations, one rural with limited exposure, and four urban with differences in the control measures that have been implemented. The researchers will collect longitudinal and time-series cross-sectional survey data to document 1) how people make interpersonal interaction and personal hygiene behavioral decisions; 2) how interpersonal interaction and personal hygiene norms change in each community during the pandemic; and 3) how the prevailing norms and shifts in those norms impact COVID-19 transmission patterns.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": 1392, "count": 13920 } } }