Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=abstract
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=abstract", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=abstract", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=abstract", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1390&sort=abstract" }, "data": [ { "type": "Grant", "id": "758", "attributes": { "award_id": "2049568", "title": "Collaborative Research: RUI: Ethics of Care and Compounded Disaster", "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": [], "program_officials": [ { "id": 1778, "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": "2021-03-01", "end_date": "2024-02-29", "award_amount": 192938, "principal_investigator": { "id": 1779, "first_name": "Jessica", "last_name": "Mulligan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 391, "ror": "https://ror.org/00rxpqe74", "name": "Providence College", "address": "", "city": "", "state": "RI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 391, "ror": "https://ror.org/00rxpqe74", "name": "Providence College", "address": "", "city": "", "state": "RI", "zip": "", "country": "United States", "approved": true }, "abstract": "When a disaster strikes, health care workers spring into response, treat the injured and aid the sick. But what happens when disasters keep happening, one after another? How do health care workers cope with the constant demands of disaster conditions? This project documents how health care workers responded to disaster conditions, cared for patients, and worked to rebuild the health care system following a major natural disaster. The research is important because disasters and health emergencies are becoming more frequent and more severe. Therefore, gathering data on how the health care work force responds emotionally to prolonged crisis conditions can aid in designing more effective responses. The broader impacts of this project include the training underrepresented students in anthropology and broadening participation for students underrepresented in science. Data from the project will be disseminated to improve healthcare delivery in post-disaster conditions. This project is jointly funded by Cultural Anthropology, and the Established Program to Stimulate Competitive Research (EPSCoR) This project examines whether the ethics of healthcare provisioning transform under conditions of \"compounded disaster,\" through an investigation of disaster recovery in Puerto Rico, whose infrastructure was devastated by Hurricane Maria in 2017, and was still operating under conditions of sustained emergency at the onset of the COVID-19 pandemic. The central question of this research is: Did health care workers' experiences during and after Hurricane Maria transform their ethics of care? The ethics of care refers to the practices and self-understanding that guide and motivate those who do the work of caring for others. The investigators hypothesize that health care workers developed a new ethics of care that drew upon pre-existing cultural features, but reflects a new sense of solidarity, greater efficacy, and shared purpose forged in the aftermath of Hurricane Maria. The investigators anticipate that this new ethics of care has evolved or transformed through compounding disasters including earthquake swarms and COVID-19. The research design includes data collection through individual interviews, remote focus groups, and on-site participant observation. The project contributes to the scientific understanding of the underlying cultural processes through which disasters transform communities; whether compounded disasters have a geometric or exponential impact on these communities; and also, if the ethics of care fundamentally change under these circumstances. This project generates theory to explain the underlying cultural processes through which (1) disasters transform communities and (2) care workers forge practices and self-understandings that aid in the process of disaster recovery.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": "764", "attributes": { "award_id": "2049565", "title": "Collaborative Research: RUI: Ethics of Care and Compounded Disaster", "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": [], "program_officials": [ { "id": 1791, "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": "2021-03-01", "end_date": "2024-02-29", "award_amount": 150736, "principal_investigator": { "id": 1792, "first_name": "Adriana M", "last_name": "Garriga-Lopez", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 393, "ror": "https://ror.org/001qst305", "name": "Kalamazoo College", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 393, "ror": "https://ror.org/001qst305", "name": "Kalamazoo College", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "When a disaster strikes, health care workers spring into response, treat the injured and aid the sick. But what happens when disasters keep happening, one after another? How do health care workers cope with the constant demands of disaster conditions? This project documents how health care workers responded to disaster conditions, cared for patients, and worked to rebuild the health care system following a major natural disaster. The research is important because disasters and health emergencies are becoming more frequent and more severe. Therefore, gathering data on how the health care work force responds emotionally to prolonged crisis conditions can aid in designing more effective responses. The broader impacts of this project include the training underrepresented students in anthropology and broadening participation for students underrepresented in science. Data from the project will be disseminated to improve healthcare delivery in post-disaster conditions. This project is jointly funded by Cultural Anthropology, and the Established Program to Stimulate Competitive Research (EPSCoR) This project examines whether the ethics of healthcare provisioning transform under conditions of \"compounded disaster,\" through an investigation of disaster recovery in Puerto Rico, whose infrastructure was devastated by Hurricane Maria in 2017, and was still operating under conditions of sustained emergency at the onset of the COVID-19 pandemic. The central question of this research is: Did health care workers' experiences during and after Hurricane Maria transform their ethics of care? The ethics of care refers to the practices and self-understanding that guide and motivate those who do the work of caring for others. The investigators hypothesize that health care workers developed a new ethics of care that drew upon pre-existing cultural features, but reflects a new sense of solidarity, greater efficacy, and shared purpose forged in the aftermath of Hurricane Maria. The investigators anticipate that this new ethics of care has evolved or transformed through compounding disasters including earthquake swarms and COVID-19. The research design includes data collection through individual interviews, remote focus groups, and on-site participant observation. The project contributes to the scientific understanding of the underlying cultural processes through which disasters transform communities; whether compounded disasters have a geometric or exponential impact on these communities; and also, if the ethics of care fundamentally change under these circumstances. This project generates theory to explain the underlying cultural processes through which (1) disasters transform communities and (2) care workers forge practices and self-understandings that aid in the process of disaster recovery.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": "11279", "attributes": { "award_id": "2329092", "title": "Collaborative Research: RUI: Ethics of Care and Compounded Disaster", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Cultural Anthropology" ], "program_reference_codes": [], "program_officials": [ { "id": 616, "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": "2023-05-01", "end_date": "2024-02-29", "award_amount": 129344, "principal_investigator": { "id": 27300, "first_name": "Adriana", "last_name": "Garriga-Lopez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2008, "ror": "", "name": "University of Puerto Rico Cayey", "address": "", "city": "", "state": "PR", "zip": "", "country": "United States", "approved": true }, "abstract": "When a disaster strikes, health care workers spring into response, treat the injured and aid the sick. But what happens when disasters keep happening, one after another? How do health care workers cope with the constant demands of disaster conditions? This project documents how health care workers responded to disaster conditions, cared for patients, and worked to rebuild the health care system following a major natural disaster. The research is important because disasters and health emergencies are becoming more frequent and more severe. Therefore, gathering data on how the health care work force responds emotionally to prolonged crisis conditions can aid in designing more effective responses. The broader impacts of this project include the training underrepresented students in anthropology and broadening participation for students underrepresented in science. Data from the project will be disseminated to improve healthcare delivery in post-disaster conditions. This project is jointly funded by Cultural Anthropology, and the Established Program to Stimulate Competitive Research (EPSCoR) \n\nThis project examines whether the ethics of healthcare provisioning transform under conditions of \"compounded disaster,\" through an investigation of disaster recovery in Puerto Rico, whose infrastructure was devastated by Hurricane Maria in 2017, and was still operating under conditions of sustained emergency at the onset of the COVID-19 pandemic. The central question of this research is: Did health care workers' experiences during and after Hurricane Maria transform their ethics of care? The ethics of care refers to the practices and self-understanding that guide and motivate those who do the work of caring for others. The investigators hypothesize that health care workers developed a new ethics of care that drew upon pre-existing cultural features, but reflects a new sense of solidarity, greater efficacy, and shared purpose forged in the aftermath of Hurricane Maria. The investigators anticipate that this new ethics of care has evolved or transformed through compounding disasters including earthquake swarms and COVID-19. The research design includes data collection through individual interviews, remote focus groups, and on-site participant observation. The project contributes to the scientific understanding of the underlying cultural processes through which disasters transform communities; whether compounded disasters have a geometric or exponential impact on these communities; and also, if the ethics of care fundamentally change under these circumstances. This project generates theory to explain the underlying cultural processes through which (1) disasters transform communities and (2) care workers forge practices and self-understandings that aid in the process of disaster recovery.\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": "1779", "attributes": { "award_id": "2027397", "title": "RAPID: Responding to a Global Pandemic--The Role of K-12 Science Teachers", "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": 4686, "first_name": "Michael", "last_name": "Steele", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2021-12-31", "award_amount": 171208, "principal_investigator": { "id": 4688, "first_name": "Patrick S", "last_name": "Smith", "orcid": "https://orcid.org/0000-0002-1680-5044", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['http://www.horizon-research.com']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4687, "first_name": "Peggy J", "last_name": "Trygstad", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 241, "ror": "", "name": "Horizon Research Inc", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "When a global health crisis emerges, students at all levels turn to their science teachers for information and, at times, reassurance, according to researchers at Horizon Research, Inc. (HRI). Science teachers serve a critically important public health function and become an important part of the nation’s response efforts. Given the magnitude of the current COVID-19 crisis, it is likely that students are bringing their questions and concerns to their science teachers. As this award is made, nearly all K–12 school buildings in the U.S. are closed, and science teachers face unprecedented challenges in carrying out the instruction for which they are responsible while simultaneously addressing students’ questions about COVID-19. Moreover, they must do this within new instructional formats. Education is crucial for helping students to understand the facts about the virus, despite much conflicting information and misinformation available. Education helps students understand and actively participate in measures to stop the spread of COVID-19. This award will support a national research study on how teachers are helping students respond to COVID-19. The findings will inform the development of curriculum materials for teaching about COVID-19, which are much needed right now, and help science teachers to adapt their instruction as they help to fulfill a critical public health function. This study will enable a better understanding of the role that science teachers can play in a national response, both now and in future crises. The research will build on a study of science teachers conducted by HRI following the Ebola outbreak of 2014. Specifically, the research will investigate (1) where teachers of science get their information about coronavirus and COVID-19; (2) what types of resources teachers find most useful; (3) what factors influence whether science teachers address COVID-19 in their instruction; and (4) how science teachers adapt their teaching in response to COVID-19. HRI will recruit a nationally representative sample of several thousand K–12 teachers of science and invite them to complete a survey about their instruction related to COVID-19, both before school buildings closed and after. Using the Theory of Planned Behavior, the survey will be constructed to identify factors that predict whether teachers take up the topic. The survey will also collect data about how teachers address the virus and its transmission with their students. HRI will disaggregate survey data by school-, class-, student-, and teacher-level variables to identify patterns in student opportunities. Survey data will be supplemented by interviews with 50 survey respondents to gather more in-depth information related to the constructs of interest. Study findings will be immediately shared through a preliminary report that focuses on the survey data; mainstream print media using press releases; and social media partnering with the National Science Teaching Association. HRI also will publish policy briefs intended as guidance for schools, districts, and states; and research articles.This RAPID award is made by the DRK-12 program in the Division of Research on Learning, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act. The Discovery Research PreK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects.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": "450", "attributes": { "award_id": "2204901", "title": "RAPID: Science Teachers as Public Health Educators: How Has the COVID-19 Pandemic Reshaped the Roles and Experiences of K-12 Science Teachers?", "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": 891, "first_name": "Michael", "last_name": "Steele", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2023-01-31", "award_amount": 185731, "principal_investigator": { "id": 893, "first_name": "Peggy J", "last_name": "Trygstad", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 241, "ror": "", "name": "Horizon Research Inc", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 892, "first_name": "Patrick S", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 241, "ror": "", "name": "Horizon Research Inc", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 241, "ror": "", "name": "Horizon Research Inc", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "When a global health crisis emerges, students at all levels turn to their science teachers for information and, at times, reassurance, according to researchers at Horizon Research, Inc. (HRI). Science teachers serve a critically important public health function and become an important part of the nation’s response efforts. Since 2020, tens of thousands of science teachers in the United States have helped students grapple with the reality of SARS-COV-2 and COVID-19. This includes not only supporting the emergent scientific learning opportunities, but also functioning as public health educators and advocates in the midst of the global pandemic. HRI explored the early role of science teachers during the pandemic with a prior RAPID project, which demonstrated that science teachers were engaged in a wide variety of educational activities with students related to the pandemic and public health education. However, now nearly two years into the global pandemic, there are a number of new topics that have arisen to explore with respect to science education, including vaccines and long-term immunity that were not able to be assessed early in the pandemic. This study will further the field's understanding of the role that science teachers play in adapting their instruction during a public health crisis, how they address emergent ideas throughout the unfolding of the pandemic, and the impacts that the pandemic has had on science teachers themselves. The research will build on the prior RAPID award (2027397) and return to the sample of science teachers studied in that context. HRI will collect survey data to explore the following questions: (1) How does the pandemic continue to influence teachers’ science instruction (for example, instructional time, instructional strategies), and how has that influence shifted? (2) How has teaching about COVID evolved? What new topics (for example, vaccines) have they taken up in thecontext of COVID? (3) What factors now exert the greatest influence on science teachers’ teaching about COVID, and how do those differ from the factors at play in the spring of 2020? (4) What are the impacts of the pandemic on science teachers themselves, including manageability of workload, opportunities for professional growth/development, physical/mental wellness, and job satisfaction? Using the Theory of Planned Behavior, the survey will be constructed to identify factors that predict whether teachers take up the topic. The survey will also collect data about how teachers address the virus and its transmission with their students. HRI will disaggregate survey data by school-, class-, student-, and teacher-level variables to identify patterns in student opportunities. Survey data will be supplemented by interviews with 50 survey respondents to gather more in-depth information related to the constructs of interest. Study findings will be immediately shared through a preliminary report that focuses on the survey data; presentations and dissemination through practitioner and research organizations; and policy briefs to the CDC and NIH to acknowledge the role of science teachers as public health advocates and encouraging those agencies to make resources available to science teachers to support this role.This RAPID award is made by the DRK-12 program in the Division of Research on Learning. The Discovery Research PreK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects.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": "11204", "attributes": { "award_id": "1R01LM014263-01", "title": "Improving the efficiency and equity of critical care allocation during a crisis with place-based disadvantage indices", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Library of Medicine (NLM)" ], "program_reference_codes": [], "program_officials": [ { "id": 26724, "first_name": "Meryl", "last_name": "Sufian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-04-01", "end_date": "2028-01-31", "award_amount": 483503, "principal_investigator": { "id": 27222, "first_name": "William F", "last_name": "Parker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "When a US hospital system is overwhelmed by disaster, Crisis Standards of Care guide the triage teams forced to choose which patients receive scarce life support treatments. Analogous to an organ allocation system, these algorithms convert ethical principles into a concrete rank ordering of candidates for Intensive Care Unit (ICU) treatments with life support allocation scores. Disasters that produce scarcity tend to fall hardest on disadvantaged communities, especially racial and ethnic minority groups. When designing algorithms to allocate scarce life support, public health officials should take this context into account. In an attempt to identify the critically ill patients with the highest likelihood of benefit from treatment, most US states would prioritize those with low Sequential Organ Failure Assessment (SOFA) scores. But SOFA was designed for patients already on life support in the ICU, using routinely measured laboratory values, drug doses, and vital signs to monitor response to treatment. Most patients have low SOFA scores when critical illness is first recognized, and SOFA cannot accurately predict the risk of death using data before life support was allocated. We demonstrated how the poor predictive performance of SOFA-based triage protocols is partially explained by underpredicting the survival of Black patients due to a miscalibrated renal component of the SOFA score. There is a clear need to develop and validate a novel life support allocation protocol designed to debias existing scores and save more lives. Place-based disadvantage indices, such as the Area Deprivation Index (ADI) and the Social Vulnerability Index, offer a potential solution. Using these validated geographical measures of neighborhood deprivation to allocate scarce healthcare resources counteracts the risk-increasing effects of social disadvantage, including disadvantage produced by racialized residential segregation. We hypothesize that a well-designed life support allocation score using place-based disadvantage indices can save more lives and mitigate healthcare inequity in a crisis. The overall objective of this project is to develop a life support allocation algorithm that accurately and equitably allocates scarce ICU treatments in a crisis. In Aim 1, we will use structural equation modeling to create an Equitable Life Support Allocation (ELSA) score, using place-based disadvantage indices to debias SOFA. In Aim 2, develop the ICU Crisis Simulation Model (ICSM), a discrete event simulation that models patient flow and survival, as a testing and evaluation environment for life support allocation protocols. In Aim 3, we will externally validate ELSA and ICSM in the National COVID Cohort Collaborative Data Enclave, which currently contains geocoded records from 14 million patients from 74 sites. Our project will address one of the most pressing challenges in applied public health ethics, producing 1) an empirically derived score to distribute life support more accurately and equitably in a crisis and 2) open-source simulation software to evaluate the efficiency and equity of life support allocation protocols.", "keywords": [ "Address", "Adult", "Algorithm Design", "Algorithms", "Area", "Black Populations", "COVID-19 pandemic", "Caring", "Chicago", "Clinical Data", "Communities", "Creatinine", "Critical Care", "Critical Illness", "Data", "Databases", "Disadvantaged", "Disasters", "Dose", "Electronic Health Record", "Ensure", "Environment", "Equation", "Ethics", "Ethnic Origin", "Ethnic group", "Evaluation", "Event", "Geographic state", "Geography", "Healthcare", "Hospitals", "Intensive Care Units", "Kidney", "Laboratories", "Legal", "Life", "Mathematics", "Measures", "Mechanics", "Modeling", "Monitor", "Nature", "Neighborhoods", "Organ failure", "Outcome", "Patients", "Performance", "Persons", "Pharmaceutical Preparations", "Politics", "Population Distributions", "Probability", "Protocols documentation", "Public Health", "Publishing", "Race", "Records", "Resource Allocation", "Resources", "Risk", "Scoring Method", "Series", "Site", "Structure", "System", "Testing", "Time", "Triage", "Validation", "black patient", "cohort", "coronavirus disease", "data enclave", "deprivation", "design", "ethnic minority population", "evaluation/testing", "falls", "health care disparity", "improved", "indexing", "models and simulation", "mortality risk", "neighborhood disadvantage", "novel", "open source", "organ allocation", "public health ethics", "racial bias", "racial minority population", "residential segregation", "risk prediction", "simulation", "simulation software", "social", "social disadvantage", "social vulnerability", "survival prediction", "systematic review", "treatment response" ], "approved": true } }, { "type": "Grant", "id": "3329", "attributes": { "award_id": "1763701", "title": "CSR: Medium: Collaborative Research: Foundations of Cache Network Operations for Content Delivery", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CSR-Computer Systems Research" ], "program_reference_codes": [], "program_officials": [ { "id": 10557, "first_name": "Erik", "last_name": "Brunvand", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2018-09-01", "end_date": "2022-08-31", "award_amount": 598000, "principal_investigator": { "id": 10558, "first_name": "Mor", "last_name": "Harchol-Balter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "When a user goes online to read the news, watch videos, buy products, play games, or use a social network, the content accessed by the user is delivered to his/her device by a large Internet-scale network called a content delivery network (or, CDN for short). A CDN may consist of hundreds of thousands of servers located in thousands of data centers around the world. Operating a large CDN is challenging due to the sheer scale and diversity of the traffic and due to the vastness of their distributed server infrastructure. This research focuses on enabling highly efficient CDN operations.\n\nThe key goals of this project are minimizing the operating cost and maximizing the robustness of the network. This project develops techniques for mapping different traffic classes to servers, so as to optimize resource usage, increase hit rates, and lower bandwidth costs. This project also develops adaptive and self-tuning systems that are robust to common sources of CDN variability, including object popularity/size, load, latency, and availability. Since CDNs are the key infrastructure enabling web, video, and other online applications, this project has a significant positive impact on the quality-of-experience of Internet users, while lowering the cost of Internet services.\n\nThe project will broaden participation by involving undergraduates in the research. Further, the project will help attract women to STEM through outreach activities, including \"Tech Nights\" and \"Roadshows\" for Middle-School girls, and including semi-annual \"Career Advice talks\" to students from high school through grad school. The project will also incorporate topics in large-scale network operations in the curriculum. Finally, the project will invest significantly in outreach and tech transfer to the CDN industry by incorporating our research in production systems and enabling students to gain real-world experience in the CDN industry through internships.\n\nAll products of the project, including research papers, software, and data, will be made available to the public. In particular, the software and data produced by the project will be archived for at least five years, and even longer when feasible. There will be two software and data repositories: one at UMass Amherst (https://www.cs.umass.edu/~ramesh/Site/CODE_DATA.html) and the other at CMU (http://www.cs.cmu.edu/~harchol/SoftwareDataRepository.html).\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": "8079", "attributes": { "award_id": "1R01GM140564-01", "title": "Merging machine learning and mechanistic models to improve prediction and inference in emerging epidemics", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 12060, "first_name": "Han", "last_name": "Nguyen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-01", "end_date": "2024-12-31", "award_amount": 429701, "principal_investigator": { "id": 23970, "first_name": "Jessie", "last_name": "Edwards", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 23970, "first_name": "Jessie", "last_name": "Edwards", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, { "id": 23971, "first_name": "Justin", "last_name": "Lessler", "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": "When an outbreak of an established or emerging infectious disease occurs we ask a standard set of questions that are critical to a lifesaving public health response: Where will future incidence occur? How many cases will there be? And where can we most effectively intervene? The proposed research is motivated by real world instances where answering these questions was critical to making practical public health decisions, and current methods came up short: from deciding if and where to build additional Ebola Treatment Units in the 2014-15 West African Ebola epidemic, to identifying priority districts where oral cholera vaccine should be used in the 2016-17 cholera outbreak in Yemen, to picking locations where sufficient cases might occur to selecting and prioritizing interventions to slow the spread of COVID-19 worldwide. Forecasts informing such decisions are typically generated either using an epidemic model that relies on knowledge of the disease transmission mechanism and epidemic theory or using a statistical model to project the expected number of cases based on the relationship between covariates and observed counts. However, both approaches are subject to limitations, particularly early in an epidemic when few cases are observed. This project is based on the overarching scientific premise that inferences that combine the strengths of mechanistic epidemic models and statistical covariate models will substantially outperform either approach alone in forecasting and making decisions to confront emerging infectious disease threats. Specifically, this project aims to (1) Develop a framework to forecast incidence in ongoing outbreaks that merges mechanistic and machine learning approaches; (2) Validate the framework using retrospective data and apply the framework to inform decision making in emerging epidemics; (3) Integrate this inferential forecasting framework into causal decision theory to optimize critical actions in the public health response to emerging epidemics; and (4) Develop accessible and extensible tools for forecasting and decision analysis in infectious disease epidemics. We will validate these approaches using rigorous simulation studies and by applying the proposed approaches to retrospective data from important recent epidemics (e.g., Ebola, Cholera and COVID-19, as mentioned above). We will prospectively apply our approach to inform the response to emerging disease threats that occur during the project period, including the ongoing COVID-19 pandemic. To ensure that the tools developed are useful, efficient, and user friendly, we will work with international humanitarian organizations responding to epidemics. Successful completion of these aims will provide a flexible and validated framework for forecasting and decision making during ongoing epidemics, while allowing for innovation in mechanistic and statistical approaches. In doing so it will provide tools to optimize responses and reduce morbidity and mortality during public health crises.", "keywords": [ "African", "Algorithms", "Area", "COVID-19", "COVID-19 pandemic", "Cholera", "Cholera Vaccine", "Communicable Diseases", "Community Health", "Cost utility", "Data", "Data Set", "Decision Analysis", "Decision Making", "Decision Theory", "Disease", "Disease Outbreaks", "Ebola", "Emerging Communicable Diseases", "Ensure", "Epidemic", "Evaluation", "Fogs", "Future", "Geographic Locations", "Incidence", "International", "Intervention", "Knowledge", "Liberia", "Link", "Location", "Machine Learning", "Methods", "Modeling", "Morbidity - disease rate", "Online Systems", "Oral", "Policies", "Public Health", "Research", "Research Personnel", "Series", "Shapes", "Statistical Algorithm", "Statistical Methods", "Statistical Models", "System", "Time", "Translating", "Update", "War", "Work", "Yemen", "base", "case-based", "curve fitting", "dashboard", "disease transmission", "experience", "flexibility", "improved", "innovation", "mortality", "multidimensional data", "programs", "prospective", "response", "simulation", "sound", "surveillance data", "theories", "tool", "transmission process", "user-friendly" ], "approved": true } }, { "type": "Grant", "id": "10435", "attributes": { "award_id": "5R01GM140564-03", "title": "Merging machine learning and mechanistic models to improve prediction and inference in emerging epidemics", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 12060, "first_name": "Han", "last_name": "Nguyen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-01", "end_date": "2024-12-31", "award_amount": 458952, "principal_investigator": { "id": 23970, "first_name": "Jessie", "last_name": "Edwards", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 23971, "first_name": "Justin", "last_name": "Lessler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 817, "ror": "", "name": "UNIV OF NORTH CAROLINA CHAPEL HILL", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "When an outbreak of an established or emerging infectious disease occurs we ask a standard set of questions that are critical to a lifesaving public health response: Where will future incidence occur? How many cases will there be? And where can we most effectively intervene? The proposed research is motivated by real world instances where answering these questions was critical to making practical public health decisions, and current methods came up short: from deciding if and where to build additional Ebola Treatment Units in the 2014-15 West African Ebola epidemic, to identifying priority districts where oral cholera vaccine should be used in the 2016-17 cholera outbreak in Yemen, to picking locations where sufficient cases might occur to selecting and prioritizing interventions to slow the spread of COVID-19 worldwide. Forecasts informing such decisions are typically generated either using an epidemic model that relies on knowledge of the disease transmission mechanism and epidemic theory or using a statistical model to project the expected number of cases based on the relationship between covariates and observed counts. However, both approaches are subject to limitations, particularly early in an epidemic when few cases are observed. This project is based on the overarching scientific premise that inferences that combine the strengths of mechanistic epidemic models and statistical covariate models will substantially outperform either approach alone in forecasting and making decisions to confront emerging infectious disease threats. Specifically, this project aims to (1) Develop a framework to forecast incidence in ongoing outbreaks that merges mechanistic and machine learning approaches; (2) Validate the framework using retrospective data and apply the framework to inform decision making in emerging epidemics; (3) Integrate this inferential forecasting framework into causal decision theory to optimize critical actions in the public health response to emerging epidemics; and (4) Develop accessible and extensible tools for forecasting and decision analysis in infectious disease epidemics. We will validate these approaches using rigorous simulation studies and by applying the proposed approaches to retrospective data from important recent epidemics (e.g., Ebola, Cholera and COVID-19, as mentioned above). We will prospectively apply our approach to inform the response to emerging disease threats that occur during the project period, including the ongoing COVID-19 pandemic. To ensure that the tools developed are useful, efficient, and user friendly, we will work with international humanitarian organizations responding to epidemics. Successful completion of these aims will provide a flexible and validated framework for forecasting and decision making during ongoing epidemics, while allowing for innovation in mechanistic and statistical approaches. In doing so it will provide tools to optimize responses and reduce morbidity and mortality during public health crises.", "keywords": [ "African", "Algorithms", "Area", "COVID-19", "COVID-19 pandemic", "Cholera", "Cholera Vaccine", "Communicable Diseases", "Community Health", "Cost utility", "Data", "Data Set", "Decision Analysis", "Decision Making", "Decision Theory", "Disease", "Disease Outbreaks", "Ebola", "Emerging Communicable Diseases", "Ensure", "Epidemic", "Evaluation", "Fogs", "Future", "Geographic Locations", "Incidence", "International", "Intervention", "Knowledge", "Liberia", "Link", "Location", "Machine Learning", "Methods", "Modeling", "Morbidity - disease rate", "Online Systems", "Oral", "Policies", "Public Health", "Research", "Research Personnel", "Series", "Shapes", "Statistical Algorithm", "Statistical Methods", "Statistical Models", "System", "Time", "Translating", "Update", "War", "Work", "Yemen", "base", "case-based", "curve fitting", "dashboard", "disease transmission", "experience", "flexibility", "improved", "innovation", "mortality", "multidimensional data", "programs", "prospective", "response", "simulation", "sound", "surveillance data", "theories", "tool", "transmission process", "user-friendly" ], "approved": true } }, { "type": "Grant", "id": "14446", "attributes": { "award_id": "2147597", "title": "SG: Uncovering the contributions of albinism to the evolution of the Mexican cavefish", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Evolutionary Processes" ], "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": "2021-08-15", "end_date": null, "award_amount": 200000, "principal_investigator": { "id": 31072, "first_name": "Johanna", "last_name": "Kowalko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 341, "ror": "https://ror.org/012afjb06", "name": "Lehigh University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "When different organisms live in similar environments, the same traits can evolve in multiple, independent populations. One fascinating example of this is the reduction or loss of pigmentation in many different cave organisms. Despite how common this pattern is in wild populations, why cave organisms lose pigmentation is not well understood. This research will study the role of pigmentation loss in the evolution of Mexican cavefish. To do so, the project will examine the relationship between pigmentation, levels of neurotransmitters in the brain, and behavioral changes that may be beneficial to animals living underground. The broader impacts of this project take advantage of the cavefish system to increase access to science education. Teachers will work with the researchers to develop new units on cavefish biology to take back to their classrooms. Additionally, the researchers will create an undergraduate class that will allow students to get authentic research experiences. Finally, undergraduates and graduate students will be hired to participate in the research. <br/><br/>This project aims to understand the effects of pigmentation-reducing mutations on behavioral traits. Evidence suggests that mutations in the oca2 gene underlie albinism. Additionally, the mutations may impact levels of sleep-regulating neurotransmitters, raising the possibility that albinism is beneficial by reducing sleep time and promoting foraging in the nutrient-poor cave environment. To test this hypothesis, the research team will focus on A. mexicanus. Some advantages of this system include the existence of multiple, independently evolved cave populations and the ability to hybridize cave and surface fish. Specifically, this project has three aims. First, the researchers will measure levels of three specific neurotransmitters (dopamine, epinephrine, norepinephrine) in multiple populations of fish. Those populations include: 1) surface fish, 2) albino (oca2-mutant), 3) non-albino cavefish, and 4) oca2-mutant surface fish. Measuring neurotransmitter levels in the different fish populations will allow the researchers to determine if enhanced neurotransmitter levels are a general property of cavefish with reduced pigmentation. For the second aim, sleep will be measured in wild-type and oca2-mutant surface fish to determine the role of oca2 in a potentially fitness-related trait. Finally, the researchers will generate hybrids by crossing individuals from cave populations and surface populations. Sleep, pigmentation and neurotransmitter levels will be measured in these hybrid fish to determine the genetic relationship between these traits. Thus, this project will provide new insight into the role of albinism in a cave environment. Further, it will shed light on fundamental principles underlying evolution in response to colonization of a novel environment.<br/><br/>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": 1391, "pages": 1424, "count": 14236 } } }