Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=program_reference_codes
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=program_reference_codes", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=program_reference_codes", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=program_reference_codes", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=program_reference_codes" }, "data": [ { "type": "Grant", "id": "1442", "attributes": { "award_id": "2029890", "title": "RAPID: Impact of the Covid-19 Pandemic on Crime and Corrections Populations", "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": 3741, "first_name": "Reginald", "last_name": "Sheehan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2022-04-30", "award_amount": 37428, "principal_investigator": { "id": 3743, "first_name": "Daniel S", "last_name": "Nagin", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3742, "first_name": "Amelia M", "last_name": "Haviland", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31234, "first_name": "Mikaela Rose", "last_name": "Meyer", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Anecdotal news accounts make clear that the COVID-19 pandemic is having profound impacts on crime and on jail and prison populations in the United States. The short-term reductions in most crimes are consistent with various opportunity-based theories of crime with fewer people on the streets and visiting places like bars. The reports of increased domestic violence align with opportunity-based theories and in addition strain-based theories. Over the longer term, however, the reported crime reduction trends may reverse themselves as people become more economically impacted. Impacts of restricted admissions and accelerated releases from local jails and prisons on crime and on infection rates, within these facilities during this pandemic, are also unknown and of policy interest. Analyses will be conducted at the level of county and city for crime and jail population impacts and at the level of the state for prison population impacts. To estimate these effects we aim to do difference-in-difference type analyses. The main objectives of this project are to provide a rapid analysis of these impacts on crime and corrections populations to be completed prior to a possible future resurgence of the pandemic, and to share with policymakers rigorous analyses that will assist in informing their decisions in dealing with the 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": "1452", "attributes": { "award_id": "2029258", "title": "RAPID: Examining Media Dependencies, Risk Perceptions, and Depressive Symptomatology during the 2020 COVID Pandemic", "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": 3770, "first_name": "Robert", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2021-05-31", "award_amount": 66453, "principal_investigator": { "id": 3771, "first_name": "Kenneth", "last_name": "Lachlan", "orcid": "https://orcid.org/0000-0002-7856-2797", "emails": "[email protected]", "private_emails": "", "keywords": "['Risk communication']", "approved": true, "websites": "['https://comm.uconn.edu/person/kenneth-lachlan/']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 257, "ror": "https://ror.org/02der9h97", "name": "University of Connecticut", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 257, "ror": "https://ror.org/02der9h97", "name": "University of Connecticut", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic presents a unique opportunity to examine risk perceptions and responses on both a national and regional scale. When faced with a major health crisis, individuals are likely to be motivated to seek information in order to alleviate anxiety and gather information about how to protect themselves. While these dependencies are well documented, less is known about the extent to which media dependency translates into desired behavior, and the extent to which other effects associated with these dependencies may help or hinder this translation. Particularly troubling is prior research supporting the notion that depressive symptomology may lead to inaction, and that reliance on different news sources may lead to variability in the perception of risk. The current study extends previous research by investigating the extent to which risk perception and motivation to take protective action are tied to specific source preferences, and the degree to which individual processing characteristics and related responses influence these relationships. The research also aims to investigate the argument that depressive symptomatology may lead to inaction under such circumstances. Further, the research team explores specific protective actions and perceptions of risk while the threat is imminent, as opposed to relying on recall. The findings contribute to our knowledge base by filling a significant gap in the social science literature on emergency response by evaluating the links between trait processing, source preferences, depressive symptomatology, and protective actions. The new knowledge is beneficial to emergency managers for message design and placement.An online survey gathers data from a nationally representative sample of 5,000 respondents to assess the key variables of interest. Participants are asked about the relative importance of varying news outlets, sources of first alerts, time spent seeking information, risk perception (including magnitude and probability), specific protective behaviors advocated by the Center for Disease Control, and depressive symptomatology. Questions also measure emotional well-being, level of involvement in the information gathered, trait need for cognition, and ruminative coping tendencies. Prior findings concerning the role of rumination in information seeking are reexamined for replication and extended to investigate the subsequent role of this processing style in both depressive symptomatology and protective actions, such as social distancing. Source preferences are reduced into clusters using Exploratory Factor Analysis and examined in terms of the impact of specific source preference clusters on risk perception and protective action.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": "1454", "attributes": { "award_id": "2029790", "title": "RAPID: Developing Social Differentiation-respecting Disease Transmission Models", "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": 3774, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2022-05-31", "award_amount": 174891, "principal_investigator": { "id": 3777, "first_name": "James W", "last_name": "Moody", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3775, "first_name": "Lisa A", "last_name": "Keister", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3776, "first_name": "Dana K", "last_name": "Pasquale", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "In this project, transmission models that account for differences in social networks and exposure opportunities are developed to gain insight into the unequal spread of COVID-19 across populations. Some areas have experienced slow to no spread of COVID-19 while other settings have been overwhelmed. Within high-volume locations, some neighborhoods have been at much greater risk than others. To account for this uneven spread, these models incorporate population differences related to social density and sociodemographic characteristics—features that shape disease exposure and ability to social distance. These models augment general understanding of how social situation affects both disease risk and the cost of disease mitigation efforts, which will allow decisionmakers to evaluate the relative costs of different health-preserving interventions and, potentially, optimize interventions that minimize economic harm while maximizing physical safety.This project aims to have accurate, flexible and scalable models for disease transmission that can account for observed social differentiation in disease spread. Simulation models are employed to meet this goal, drawing on best estimates from the COVID-19 pandemic for disease-specific infection parameters and rates of transitioning into hospitalization, death, or recovery. Modeling occurs on two levels: Agent-Based Models (ABMs) and small-area cell-based simulation models. ABMs are constructed from social network data and allow for maximum flexibility, being tunable to different types of populations, ranging from rural communities in developing nations to dense urban centers. Small-area (census block group) cell-based simulation models, which translate network structure to interaction probabilities based on demographic and economic similarity profiles, include population differentiation but scale to the national level. These two modeling strategies complement each other and can be used to evaluate different mitigation strategies for both health effectiveness (lives saved) and economic hardship.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": "1464", "attributes": { "award_id": "2029963", "title": "RAPID: Assessing the Social Consequences 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", "7914", "9179" ], "program_officials": [ { "id": 3801, "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": "2020-05-01", "end_date": "2021-04-30", "award_amount": 110399, "principal_investigator": { "id": 3804, "first_name": "Long", "last_name": "Doan", "orcid": "https://orcid.org/0000-0001-9875-1037", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3802, "first_name": "Jessica", "last_name": "Fish", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3803, "first_name": "Liana", "last_name": "Sayer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "This project examines the impacts of COVID-19 and states’ and local governments’ social distancing directives on behavior, time spent with others, use of technology, and mental and physical wellbeing. The objective of the project is to investigate these daily life impacts in real time and to analyze how these impacts are affected by sociodemographic characteristics that affect time use and well-being. Data are leveraged from several hundred respondents’ daily time use before the pandemic along with data collected during and after the pandemic to create a natural experiment that isolates the effects of the pandemic on changes in behavior. Among the products of this research are evidence-based recommendations to address the social consequences of the pandemic.This project collects data for the second and third waves of a three-wave panel study, the second wave during the pandemic with shelter-at-home and lockdown orders in place and the third wave after the pandemic has subsided and orders have been relaxed. Data for these two waves consist of survey responses and 24-hour time diaries collected from 2,000 respondents from online crowdsourcing platforms. This sample includes a smaller sample from whom data were collected before the pandemic. Data are collected on sociodemographics, typical sleep, work, and exercise patterns, and arrangements for housework and carework to investigate effects on time use and wellbeing.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": "1473", "attributes": { "award_id": "2031287", "title": "RAPID: Analyzing forced habit change from COVID-19 using large-scale data", "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": 3826, "first_name": "Nancy", "last_name": "Lutz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-08-31", "award_amount": 174313, "principal_investigator": { "id": 3830, "first_name": "Colin F", "last_name": "Camerer", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 211, "ror": "https://ror.org/05dxps055", "name": "California Institute of Technology", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3827, "first_name": "Matthew S", "last_name": "Shum", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3828, "first_name": "Yi", "last_name": "Xin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3829, "first_name": "Lawrence", "last_name": "Jin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 211, "ror": "https://ror.org/05dxps055", "name": "California Institute of Technology", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "During the COVID-19 pandemic, people were forced to try new routine at school, work, and home — “forced exploration”. That is, habits used to guide how we work, conduct our daily routine, exercise, eat, go to school, and interact with friends and neighbors, Habits are formed to make the same routines effortless, to save time and mental effort, when routine choices work well. However, while the mental savings from habits are a benefit, when people are choosing habitually, they may not be exploring other options which could be even better— that is the hidden cost of habit. Forced exploration can actually be beneficial if it shows people better ways of school, work, and social interaction. This kind of exploration is like going to your favorite restaurant and finding out they have run out of your favorite dish — now you have to try something new, which you would not have explored without the disruption. This wave of forced exploration raises important questions: What new habits are formed that will persist— what will be the “new normal”? Consider, for example, wearing a face-mask outside of the house. This is exactly the kind of “muscle memory” behavior that usually habitizes— it can be triggered while stepping out of your car, or entering a store, and quickly becomes automatic and effortless. Whether a lot of other people are wearing masks or not can also be a trigger that prompts habit (in either direction).The same question arises across the board: Will people go back to movie theaters (or stay home with streaming)? Will restaurants fully reopen or will home delivery take over? Will knowledge firms switch to more remote “tele-work”? Will schools find better mixtures of home learning and in-school activity? This project will analyze two different kinds of big data to test whether or not this kind of forced exploration really does result in new habits.In social sciences, habits are usually modelled mathematically using a simple equation in which the more an activity has been done in the past, the more it is done in the future. This is called a \"reduced form\" approach because it reduces a biologically complicated mechanism to something much simpler. It is a good starting point but cannot answer questions such as \"What if past behavior is disrupted?” This research project uses a new approach to habits based on animal learning and human cognitive neuroscience. The starting point is that habits have developed to save effort ⎯— both physical and mental. The “neural autopilot” framework proposed here predicts that individuals develop habits for actions which, after repeated decisions, have proven to be reliably rewarding. Such habitual behavior drains fewer physical and mental resources. At the same time, when people are habitized⎯- about exercise, eating, or work — they ignore new goods and activities they would prefer if they actually tried them. While the neural autopilot approach has been tested in many lab studies of animal and human habituation, it has never been systematically explored using a large amount of data about how people actually behave in everyday life. An ideal test of this model is in a field setting where choice sets are artificially truncated, so people resort to new choices; and that is exactly what happened during the ongoing lockdowns. This project will use data from Weibo chat data and Fitbit fitness and sleep tracking. These large sets of data contain fine-grained measurements of behavior. Using this data, we will develop and test a statistical neural autopilot model, to recover values for the model’s main parameters. The parameters are numbers that measure, for each person, how fast habits are formed and the threshold to break out of a habit and explore something that might be better. The estimated parameter values will be used to make predictions about which habits acquired during the pandemic will persist, and which behavior will revert to pre-pandemic 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 } }, { "type": "Grant", "id": "1474", "attributes": { "award_id": "2035682", "title": "RAPID: Higher Order Beliefs during a Pandemic: Theory and Evidence to Test the Effects of Public Health Campaigns", "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": 3831, "first_name": "Kwabena", "last_name": "Gyimah-Brempong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2022-06-30", "award_amount": 198852, "principal_investigator": { "id": 3833, "first_name": "Ethan Bueno de", "last_name": "Mesquita", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3832, "first_name": "Adam P", "last_name": "Zelizer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Social distancing is critical to slowing the spread of COVID-19 pandemic, which threatens the nation’s health, economy, and national defense. This research project studies people’s strategic behavior in deciding to follow public health rules or not. The project uses sophisticated economic theory and empirical methods to study strategic considerations that may hamper or facilitate compliance with public health guidelines to reduce the spread of COVID-19. One person’s choice to comply with social distancing regulation influences the costs and benefits to others doing so. As a result, individual behavior depends on beliefs about others’ compliance and on social norms regarding compliance. This project increases understanding of individual behavior in group settings and highlights the conditions under which benefits of publicity in mass media campaigns, and provides hundreds of thousands of public health mailers to American residents may be beneficial. The results of this research project will help public health authorities to craft more efficient public information campaigns. This research project uses theory and empirical methods based on a large data set from Cook county to investigate how beliefs in other peoples’ response influence one’s decision to comply with public health rules. The theory extends previous research on two strategic considerations, conformity, and free-riding, that influence behavior by incorporating the related phenomenon of publicity. Publicity is found to amplify pro-social behavior only when conformity dominates free-riding; otherwise publicity will lessen the effectiveness of an informational campaign. The empirics analyze a vast public health campaign in Cook County, Illinois. The campaign will provide public health best practices directly to hundreds of thousands of Cook County households. Using a mix of public information and a voluntary survey, we will evaluate the effectiveness of the public health campaign in promoting behaviors identified as decreasing the risk of transmitting COVID-19. The results of this research project will help public health authorities to craft more efficient public information campaigns.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": "1475", "attributes": { "award_id": "2032452", "title": "RAPID: Effects of COVID-19 on Community Solidarity", "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": 3834, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-06-30", "award_amount": 67146, "principal_investigator": { "id": 3836, "first_name": "James", "last_name": "Hawdon", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3835, "first_name": "Ashley", "last_name": "Reichelmann", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "In this project, how health pandemics affect levels of community solidarity, how these levels differ from other tragedies, and whether solidarity can be generated and sustained using virtual means are explored. Tragedies, such as mass shootings and natural disasters, often bring communities together in solidarity as their residents gather to mourn and comfort one another. While the COVID-19 tragedy has created an environment ripe for solidarity, coming together in mutual support – the typical method for generating and sustaining solidarity – has not been possible because of the widely adopted social distancing guidelines that has prevented being physically close. Findings promote national health and welfare by furthering understanding of how feelings of community and belonging are promoted even when we cannot be in physical proximity. The fundamental goals of the project are to explore whether the solidarity-generating effects of natural disasters are similar to those of man-made disasters, if and how communicating virtually can promote and sustain solidarity in a way comparable to interaction in the physical world, and what role culture-building institutions can play in buffering the effects of a pandemic. Four waves of survey panel data from students from one particular university and a national sample of college-aged students are used (1) to compare current levels of community solidarity in at the university to levels of community solidarity before and after a tragic event that occurred on the campus; (2) to document changes in levels of solidarity over time to determine if trajectories are similar to those witnessed after other tragedies; (3) to track the various types of virtual communication that are associated with higher levels of community solidarity and sustained levels of solidarity; (4) to explore the relationships between sociodemographic variables and feelings of solidarity; (5) to map respondents’ involvement in and commitment to the campus culture and their feelings of community solidarity over time; and (6) to compare levels of solidarity among this university’s students with those of other college-aged Americans over time.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": "1483", "attributes": { "award_id": "2028331", "title": "RAPID: Pandemic School Closures and Teacher-Student Relationships", "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": 3856, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2023-07-31", "award_amount": 150763, "principal_investigator": { "id": 3857, "first_name": "Allison", "last_name": "Pugh", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 517, "ror": "", "name": "University of Virginia Main Campus", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 517, "ror": "", "name": "University of Virginia Main Campus", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project assesses how new efforts and strategies for action, developed due to the COVID-19 pandemic, challenge or compound pre-existing differences among students. Existing scholarship predicts that during crises, people and organizations develop new strategies for action. As a consequence of the COVID-19 pandemic, more than half of America’s schools have closed down and transitioned to online learning. This constitutes a unique natural experiment in how a crisis and uncertainty can affect teacher-student relationships (TSRs) and how these in turn can affect differences. The current crisis may strengthen TSRs as teachers and schools reach out to students in novel ways and normally non-academic matters such as student’s health and access to the internet at home become priorities. This study will help policymakers and school officials understand the impact of the COVID-19 pandemic and resulting school closures on different groups of students, informing efforts to redress the effects of this crisis and plan for future disasters.This project expands upon six months of ethnographic observations in two Virginia high schools (conducted prior to school closures) with online observations, interviews and weekly student time diaries and photojournals during the spring and summer of 2020, as well as resumed in-person data collection during fall 2020. Broadly, this project advances knowledge by showing 1) how school and teacher strategies to reach students during this crisis are received; 2) how TSRs change during moments of crisis and 3) how these changes inform differences in student experiences and outcomes. Specific research questions include: (1) How do TSRs shape the impact of the crisis on student engagement and outcomes? (2) How does the ongoing impact of TSRs vary by student background? (3) How does school response to the crisis affect teachers’ connections with students? and (4) How does the impact of this response vary based on student background? To address these questions, this study’s data collection includes eight months of observations (both virtual and in-person when schools re-open), collection of weekly student time diaries and photojournals (N=80), and in-depth semi-structured interviews with administrators (N=20), students (N=120) and teachers (N=60).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": "1534", "attributes": { "award_id": "2029696", "title": "Collaborative Research: RAPID: Spatial Modeling of Immune Response to Multifocal SARS-CoV-2 Viral Lung Infection", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914", "9179" ], "program_officials": [ { "id": 4002, "first_name": "Kathryn", "last_name": "Dickson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2021-05-31", "award_amount": 79864, "principal_investigator": { "id": 4003, "first_name": "Stephanie", "last_name": "Forrest", "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": "COVID-19 recently emerged as a worldwide pandemic, causing untold human suffering and severe economic disruptions. How individual immune systems respond to a novel coronavirus, for example, why some individuals clear infection efficiently while others do not is not known. This project seeks to understand how immune cells, specifically T cells, find cells infected with virus that are dispersed in the lung. It will address how spatial distributions of infected cells and movement patterns of T cells through complex lung structures determine the course of infection. The project will develop the Spatial Immunological Model of Coronavirus (SIM-Cov), a simulation model for studying these effects and improving understanding of how the immune system controls infection by coronaviruses. The model will take computed tomography (CT) scans of an infected human lung as input, as well as biological data on how T cells interact with the virus and infected lung cells. The model will predict the course of infection in the form of visually intuitive movies showing how the infection progresses through time in different individuals. By modeling variability in individuals’ infectious rates over time, SIM-Cov will improve our understanding of why the severity of COVID-19 varies so much among individuals. The model and movies will be publicly accessible, and incorporated into educational materials for high school and college students. The project will also train two graduate and one undergraduate students in interdisciplinary research.One gap in understanding infection dynamics of the novel coronavirus SARS-CoV-2 is why the severity of infection varies so much among individuals. This project addresses that gap by incorporating the role played by spatial-temporal dynamics in within-host infections and immune control, particularly the role of T cells which are required for viral clearance. Most quantitative models of viral infection use differential equations or stochastic models and do not account for the spatial distribution of infected cells or T cell movement patterns. The project addresses this gap by developing a three-dimensional spatial model of the whole lung (SIM-Cov) that tests how spatial interactions between T cells and virus affect viral growth, load and clearance within the lungs. Ultimately, these within-host factors contribute to the rate of clearance within a single host and transmission between hosts. SIM-Cov will be parameterized and validated with empirical imaging data (CT scans of SARS-infected patients) and the emerging literature on SARS-CoV-2 and immune responses. SIM-Cov will model the lung microenvironment, including vasculature and epithelium surrounding the airways and alveolar spaces, the spatial and temporal spread of virus throughout the lung, and the spatial arrangement and movement of T cells. The project will have broad-ranging impacts for understanding coronavirus infection dynamics and educational impacts through dissemination of the model and movies produced in the project, as well as the engagement of three students in interdisciplinary research.This RAPID award is made by the Physiological Mechanisms and Biomechanics Program and the Symbiosis, Defense, and Self-recognition Program in the BIO Division of Integrative Organismal Systems, and by the Established Program to Stimulate Competitive Research (EPSCoR), using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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": "1536", "attributes": { "award_id": "2029774", "title": "RAPID: Comparative genomics of SARS-CoV-2 susceptibility and immune defense in mammals", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914", "9179" ], "program_officials": [ { "id": 4007, "first_name": "Joanna", "last_name": "Shisler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 199767, "principal_investigator": { "id": 4009, "first_name": "Elinor K", "last_name": "Karlsson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 613, "ror": "https://ror.org/0464eyp60", "name": "University of Massachusetts Medical School", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4008, "first_name": "Diane P", "last_name": "Genereux", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 613, "ror": "https://ror.org/0464eyp60", "name": "University of Massachusetts Medical School", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The goal of this project is to compare genomes of hundreds of mammal species, finding differences in DNA that distinguish species resistant to SARS-CoV-2 from those that are very susceptible. This information is needed to understand how the current SARS-CoV-2 virus spread to humans and to identify potential host animals (e.g., pet, livestock, and pest species) that may be susceptible to SARS-CoV-2 in the USA. SARS-CoV-2, the cause of the COVID-19 pandemic, can infect diverse species of animals. There is a variation in susceptibility to and severity of disease between species. This variation suggests that some species have genetic differences that dictate susceptibility to COVID-19. This work will identify how coronaviruses adapt to new host species, information that will help predict and control future coronavirus outbreaks. Funding will support training a graduate student in research, thereby training the next generation of the bioeconomy workforce. This project will investigate how the host genome shapes host-pathogen interactions, and how coronaviruses like SARS-CoV-2 evolve to exploit new hosts. The researchers will compare existing genomic data for hundreds of mammals using three complementary approaches: (1) Measure structural and sequence homology in two host proteins, ACE2 and TMPRSS2, necessary for infection in humans; (2) Analyze existing RNA-seq datasets to (a) identify species with co-expression of ACE2 and TMPRSS2, and potentially other proteases implicated in infection, in the same tissue, and (b) search for incidental coronaviral sequence data from diverse mammalian species; (3) Test for variants in evolutionarily conserved elements that are correlated with species susceptibility, using forward genomics. With these analyses, the researchers will identify species with potential as reservoirs for SARS-CoV-2 viral spillback into humans, and those that are promising systems for investigating SARS-CoV-2 evolution, host defenses, and host-pathogen interactions. This RAPID award is made by the Physiological and Structural Systems Cluster in the BIO Division of Integrative Organismal Systems, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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": 1392, "pages": 1405, "count": 14046 } } }