Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=title
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=title", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=title", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=title", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=title" }, "data": [ { "type": "Grant", "id": "14187", "attributes": { "award_id": "2100027", "title": "Using Cloud Technologies to Develop the Data Analysis Skills of Community College Students", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Advanced Tech Education Prog" ], "program_reference_codes": [], "program_officials": [ { "id": 1983, "first_name": "Paul", "last_name": "Tymann", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 299994, "principal_investigator": { "id": 30760, "first_name": "Esma", "last_name": "Yildirim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30760, "first_name": "Esma", "last_name": "Yildirim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1096, "ror": "", "name": "CUNY Queensborough Community College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The greater New York City (NYC) area is one of the world’s leading financial and cultural centers. High-tech jobs are a key driver of NYC’s place in the US economy and the need for high tech workers is growing. From 2008 to 2018, jobs in this sector rose 45%, with jobs in the data analytics category representing 30% of that total growth. The current rate at which students graduate with skills in data analytics is not keeping pace with the demand for skilled data analytics technicians. This project aims to increase the number of students graduating with the skills necessary to enter the data analytics workforce. It will do so by improving the data analytics knowledge and skills of students at a diverse urban community college. The project intends to increase the participation of women and individuals from communities that are not yet equitably represented in the technical workforce. It is expected that the project will help prepare students for the predicted employment opportunities and contribute to better productivity, problem solving, and effectiveness in the workplace.<br/><br/>Working in collaboration with the Business Industry Leadership Team at Queensborough Community College, the project team intends to enhance the ability of the College to recruit, educate, and graduate a diverse group of students to help meet regional employment needs. Participating students will be offered a summer boot camp in Data Science/Analysis, a year-long undergraduate research experience following the summer camp, and a series of workshops focused on skills for internship applications and job interviews. To meet role models and increase their sense of belonging in Data Science/Analysis, students will also participate in a seminar series focused on Data Science/Analysis careers that will feature professionals from diverse backgrounds and from both academia and industry. The project will also offer preparation for the AWS Certified Cloud Practitioner certification. It is expected that this initiative will lay the foundation to establish a degree program in Data Science/Analysis at Queensborough Community College. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy.<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 } }, { "type": "Grant", "id": "10067", "attributes": { "award_id": "2155072", "title": "Using Computational Modeling to Transform Assessments of Creativity in Engineering Design", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "ECR-EHR Core Research" ], "program_reference_codes": [], "program_officials": [ { "id": 3698, "first_name": "Bonnie", "last_name": "Green", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2025-08-31", "award_amount": 318879, "principal_investigator": { "id": 25945, "first_name": "Mark", "last_name": "Fuge", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "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 collaborative project from research teams at Pennsylvania State University, University of Maryland, and Washington and Lee University focuses on measuring creativity in undergraduate engineering education. The ability to think creatively is essential for success in STEM fields, particularly engineering, which requires designing solutions to complex problems that often have no single or \"correct\" solution. The Next Generation Science Standards identify creative thinking skills, such as problem solving and flexibility, as core competencies for modern STEM education. Yet educators are not currently equipped with adequate tools to assess creativity in their classrooms. To effectively prepare the STEM workforce, there is a critical need for assessment tools that educators and researchers can use to identify what works in STEM education to foster creativity. Current creativity tests present significant challenges for STEM educators, including (in-person) paper administration and, perhaps most problematically, manual scoring that requires teachers to count and code thousands of responses—a labor-intensive and often costly process, particularly for under-resourced schools. In light of the increasingly diverse student population, the availability of creativity tests that measure student ability fairly and consistently, regardless of race or ethnicity, is even more critical for equity of opportunity in STEM education. This project seeks to create an online platform for measuring creativity in engineering design that educators can use to cater to the needs of all their students. The tool will allow educators to administer a range of engineering creativity tasks and automatically calculate creativity scores. This project fits the intent of the ECR program to facilitate \"the development, refinement, and testing of new education research, measurement, and evaluation methodologies.\" It addresses the ECR research track, \"Research on STEM Learning and Learning Environments,\" and has additional impacts for \"Research on Broadening Participation in STEM Fields\" by designing inclusive and culturally and linguistically diverse assessment tools targeted to students who remain underrepresented in the pursuit of STEM courses of study and English as second language speakers.\n\nTwo aims guide this project. First is to build an online platform for large-scale engineering design assessment — validating all platform tasks with undergraduate engineering students — to allow teachers and researchers to easily assess creativity, automatically compute creativity metrics, and generate customizable student reports. Second is to apply the platform in an undergraduate design course at Penn State that includes a 3-week Creativity Module (with lessons and exercises on creativity in engineering design) to obtain valuable platform usability data from both instructors and students, while evaluating a promising undergraduate course intended to promote creativity in engineering design. The team will apply recent advances in computational modeling and machine learning — including active learning of design sketches and distributional semantic modeling of text-based responses to creative problem solving tasks. It is expected that this approach will streamline educational assessment of creativity, resulting in a user-friendly technology to assist STEM educators in the classroom. The novel computational tools developed in this project will advance knowledge and understanding for creativity psychometric assessment and across different fields (not only engineering). The PI team will also design assessment tools that are culturally responsive and minimally biased — especially for the growing number of students who speak English as a second language — and collaborate with STEM educators to maximize the usability of the platform in their classrooms. The online platform and course materials will be publicly available, facilitating the national transition to remote education and research (accelerated by the current pandemic) by providing online resources for STEM teachers and researchers across the country.\n\nThis project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.\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": "10076", "attributes": { "award_id": "2155070", "title": "Using Computational Modeling to Transform Assessments of Creativity in Engineering Design", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "ECR-EHR Core Research" ], "program_reference_codes": [], "program_officials": [ { "id": 3698, "first_name": "Bonnie", "last_name": "Green", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2025-08-31", "award_amount": 1117805, "principal_investigator": { "id": 25960, "first_name": "Roger", "last_name": "Beaty", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25958, "first_name": "Janet van", "last_name": "Hell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25959, "first_name": "Scarlett", "last_name": "Miller", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This collaborative project from research teams at Pennsylvania State University, University of Maryland, and Washington and Lee University focuses on measuring creativity in undergraduate engineering education. The ability to think creatively is essential for success in STEM fields, particularly engineering, which requires designing solutions to complex problems that often have no single or \"correct\" solution. The Next Generation Science Standards identify creative thinking skills, such as problem solving and flexibility, as core competencies for modern STEM education. Yet educators are not currently equipped with adequate tools to assess creativity in their classrooms. To effectively prepare the STEM workforce, there is a critical need for assessment tools that educators and researchers can use to identify what works in STEM education to foster creativity. Current creativity tests present significant challenges for STEM educators, including (in-person) paper administration and, perhaps most problematically, manual scoring that requires teachers to count and code thousands of responses—a labor-intensive and often costly process, particularly for under-resourced schools. In light of the increasingly diverse student population, the availability of creativity tests that measure student ability fairly and consistently, regardless of race or ethnicity, is even more critical for equity of opportunity in STEM education. This project seeks to create an online platform for measuring creativity in engineering design that educators can use to cater to the needs of all their students. The tool will allow educators to administer a range of engineering creativity tasks and automatically calculate creativity scores. This project fits the intent of the ECR program to facilitate \"the development, refinement, and testing of new education research, measurement, and evaluation methodologies.\" It addresses the ECR research track, \"Research on STEM Learning and Learning Environments,\" and has additional impacts for \"Research on Broadening Participation in STEM Fields\" by designing inclusive and culturally and linguistically diverse assessment tools targeted to students who remain underrepresented in the pursuit of STEM courses of study and English as second language speakers.\n\nTwo aims guide this project. First is to build an online platform for large-scale engineering design assessment — validating all platform tasks with undergraduate engineering students — to allow teachers and researchers to easily assess creativity, automatically compute creativity metrics, and generate customizable student reports. Second is to apply the platform in an undergraduate design course at Penn State that includes a 3-week Creativity Module (with lessons and exercises on creativity in engineering design) to obtain valuable platform usability data from both instructors and students, while evaluating a promising undergraduate course intended to promote creativity in engineering design. The team will apply recent advances in computational modeling and machine learning — including active learning of design sketches and distributional semantic modeling of text-based responses to creative problem solving tasks. It is expected that this approach will streamline educational assessment of creativity, resulting in a user-friendly technology to assist STEM educators in the classroom. The novel computational tools developed in this project will advance knowledge and understanding for creativity psychometric assessment and across different fields (not only engineering). The PI team will also design assessment tools that are culturally responsive and minimally biased — especially for the growing number of students who speak English as a second language — and collaborate with STEM educators to maximize the usability of the platform in their classrooms. The online platform and course materials will be publicly available, facilitating the national transition to remote education and research (accelerated by the current pandemic) by providing online resources for STEM teachers and researchers across the country.\n\nThis project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.\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": "10089", "attributes": { "award_id": "2155071", "title": "Using Computational Modeling to Transform Assessments of Creativity in Engineering Design", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "ECR-EHR Core Research" ], "program_reference_codes": [], "program_officials": [ { "id": 3698, "first_name": "Bonnie", "last_name": "Green", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2025-08-31", "award_amount": 63316, "principal_investigator": { "id": 25981, "first_name": "Dan", "last_name": "Johnson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1884, "ror": "https://ror.org/05r9xgf14", "name": "Washington and Lee University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "This collaborative project from research teams at Pennsylvania State University, University of Maryland, and Washington and Lee University focuses on measuring creativity in undergraduate engineering education. The ability to think creatively is essential for success in STEM fields, particularly engineering, which requires designing solutions to complex problems that often have no single or \"correct\" solution. The Next Generation Science Standards identify creative thinking skills, such as problem solving and flexibility, as core competencies for modern STEM education. Yet educators are not currently equipped with adequate tools to assess creativity in their classrooms. To effectively prepare the STEM workforce, there is a critical need for assessment tools that educators and researchers can use to identify what works in STEM education to foster creativity. Current creativity tests present significant challenges for STEM educators, including (in-person) paper administration and, perhaps most problematically, manual scoring that requires teachers to count and code thousands of responses—a labor-intensive and often costly process, particularly for under-resourced schools. In light of the increasingly diverse student population, the availability of creativity tests that measure student ability fairly and consistently, regardless of race or ethnicity, is even more critical for equity of opportunity in STEM education. This project seeks to create an online platform for measuring creativity in engineering design that educators can use to cater to the needs of all their students. The tool will allow educators to administer a range of engineering creativity tasks and automatically calculate creativity scores. This project fits the intent of the ECR program to facilitate \"the development, refinement, and testing of new education research, measurement, and evaluation methodologies.\" It addresses the ECR research track, \"Research on STEM Learning and Learning Environments,\" and has additional impacts for \"Research on Broadening Participation in STEM Fields\" by designing inclusive and culturally and linguistically diverse assessment tools targeted to students who remain underrepresented in the pursuit of STEM courses of study and English as second language speakers.\n\nTwo aims guide this project. First is to build an online platform for large-scale engineering design assessment — validating all platform tasks with undergraduate engineering students — to allow teachers and researchers to easily assess creativity, automatically compute creativity metrics, and generate customizable student reports. Second is to apply the platform in an undergraduate design course at Penn State that includes a 3-week Creativity Module (with lessons and exercises on creativity in engineering design) to obtain valuable platform usability data from both instructors and students, while evaluating a promising undergraduate course intended to promote creativity in engineering design. The team will apply recent advances in computational modeling and machine learning — including active learning of design sketches and distributional semantic modeling of text-based responses to creative problem solving tasks. It is expected that this approach will streamline educational assessment of creativity, resulting in a user-friendly technology to assist STEM educators in the classroom. The novel computational tools developed in this project will advance knowledge and understanding for creativity psychometric assessment and across different fields (not only engineering). The PI team will also design assessment tools that are culturally responsive and minimally biased — especially for the growing number of students who speak English as a second language — and collaborate with STEM educators to maximize the usability of the platform in their classrooms. The online platform and course materials will be publicly available, facilitating the national transition to remote education and research (accelerated by the current pandemic) by providing online resources for STEM teachers and researchers across the country.\n\nThis project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.\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": "8181", "attributes": { "award_id": "3OT2HD107544-01S1", "title": "Using COVID-19 testing and risk communication strategies to accelerate students return to school", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 8321, "first_name": "CHRISTOPHER CHARLES", "last_name": "Lindsey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-15", "end_date": "2023-03-31", "award_amount": 1091085, "principal_investigator": { "id": 11528, "first_name": "Linda K", "last_name": "Ko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": null, "keywords": [], "approved": true } }, { "type": "Grant", "id": "3556", "attributes": { "award_id": "1OT2HD107544-01", "title": "Using COVID-19 testing and risk communication strategies to accelerate students return to school", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 11526, "first_name": "CHRISTOPHER CHARLES", "last_name": "Lindsey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-15", "end_date": "2023-03-31", "award_amount": 2908361, "principal_investigator": { "id": 11527, "first_name": "Helen Ying-hui", "last_name": "Chu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 11528, "first_name": "Linda K", "last_name": "Ko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": null, "keywords": [], "approved": true } }, { "type": "Grant", "id": "14808", "attributes": { "award_id": "1K01MH132899-01A1", "title": "Using Data Science to Quantify the Impact of Misinformation, Mistrust, and Other Key Psychosocial Factors on Vaccine Hesitancy Among Vulnerable People Experiencing Psychopathology", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Mental Health (NIMH)" ], "program_reference_codes": [], "program_officials": [ { "id": 26585, "first_name": "Maggie", "last_name": "Sweeney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-01", "end_date": "2028-05-31", "award_amount": 187261, "principal_investigator": { "id": 31484, "first_name": "Michael V", "last_name": "Bronstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 764, "ror": "https://ror.org/017zqws13", "name": "University of Minnesota", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "People with severe mental illness (SMI) are ~3x more likely to contract and ~7x more likely to die from vaccine- preventable disease. And yet, vaccine uptake is significantly lower among people with SMI, likely due to greater vaccine delay/refusal despite availability (“hesitancy”). Critical knowledge gaps – including extremely limited knowledge of whether and how unique features of SMI modulate hesitancy – impede selection of interventions on vaccination for people with SMI. This K01’s purpose is to determine unique reasons for hesitancy among people with SMI (Aim 1), reveal causal pathways to hesitancy in people with SMI and forecast which variables in these pathways are likely the best intervention targets (Aim 2), and examine the impact of education about herd immunity – which improves vaccine intentions in the general population – on vaccine intentions and uptake in people with SMI (Aim 3). In Study 1, we will recruit people with SMI and two comparison groups: people with depression/anxiety and healthy people. Participants will complete measures of SARS-CoV-2 and influenza vaccine intentions/uptake, of factors that may be uniquely relevant to hesitancy in people with SMI, and of variables implicated in general population models of vaccine hesitancy. We will compare groups’ willingness to vaccinate, their level of concern about vaccines’ dangerousness, and their endorsement of anti-vaccine information (Aim 1). We will use active-learning causal discovery algorithms and intervention calculus to reveal causal pathways to hesitancy in people with SMI, quantify the lower-bound total causal effect of variables in these pathways on hesitancy, and prescribe future experiments to resolve any uncertainties in these causal pathways (Aim 2). In Study 2, we will use established natural language processing tools to analyze rates of and attitudes to anti- vaccine misinformation in Tweets from Study 1 participants. This analysis will complement our laboratory assessments of anti-vaccine misinformation endorsement for Aim 1. In Study 3, we will recruit a subset of participants from Study 1’s healthy and SMI groups. We will compare the effects of education about herd immunity across these groups, with the goal of identifying differential efficacy and examining the need to tailor existing interventions to people with SMI (Aim 3). Our rigorous characterization of unique aspects of vaccine hesitancy among people with SMI, along with causal pathways we reveal, our corresponding predictions about the impact of manipulating variables in these pathways, and our quantification of herd immunity education’s effect on vaccine decisions, will provide critical road-maps for empirically-informed interventions on hesitancy in people with mental illness. These road-maps will move us closer to a world where effective, evidence-based interventions on hesitancy are deployed to protect people with SMI from vaccine-preventable disease.", "keywords": [ "2019-nCoV", "Active Learning", "Address", "Algorithms", "Analysis of Variance", "Anxiety", "Attitude", "Behavior", "Binding", "COVID-19", "Calculi", "Cessation of life", "Complement", "Computer Models", "Contracts", "Dangerousness", "Data", "Data Analyses", "Data Science", "Decision Theory", "Disease", "Education", "Effectiveness", "Evidence based intervention", "Foundations", "Frequencies", "Fright", "Future", "General Population", "Goals", "Graph", "Health", "Herd Immunity", "Illness impact", "Infection", "Intervention", "Intervention Trial", "Knowledge", "Laboratories", "Learning", "Measures", "Mental Depression", "Mental disorders", "Minnesota", "Misinformation", "Modeling", "Morbidity - disease rate", "Motivation", "National Institute of Mental Health", "Natural Language Processing", "Outcome", "Paranoia", "Participant", "Pathway interactions", "Persons", "Pilot Projects", "Population", "Positioning Attribute", "Psychological Models", "Psychopathology", "Psychosocial Factor", "Public Health", "Randomized Controlled Trials", "Research", "Severities", "Surveys", "Symptoms", "Testing", "Training", "Translating", "Trust", "Twitter", "Uncertainty", "Vaccinated", "Vaccination", "Vaccines", "Vulnerable Populations", "Work", "adverse childhood events", "analytical tool", "comparison group", "cost", "efficacious intervention", "efficacy evaluation", "evidence base", "experience", "experimental study", "health belief", "ideation", "improved", "infection risk", "influenza virus vaccine", "machine learning algorithm", "machine learning method", "mortality", "neglect", "news", "recruit", "severe mental illness", "skill acquisition", "theories", "tool", "uptake", "vaccine acceptance", "vaccine hesitancy", "willingness" ], "approved": true } }, { "type": "Grant", "id": "9737", "attributes": { "award_id": "1R01AI170137-01", "title": "Using Disadvantage Indices to Address Structural Racism and Discrimination in Pandemic Vaccine Allocation and Beyond: Defining the Shape of a Novel Paradigm to Promote Health Equity", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 6243, "first_name": "BROOKE ALLISON", "last_name": "Bozick", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2027-06-30", "award_amount": 721177, "principal_investigator": { "id": 25578, "first_name": "Thomas Harald", "last_name": "Schmidt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25579, "first_name": "Ruqaiijah", "last_name": "Yearby", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Covid-19 exposed inequitable healthcare access and disparate health outcomes of people of color, that are due to structural racism and discrimination (SRD)—but in an unprecedented turn, policymakers also deployed a major novel tool to address SRD within, and likely outside of the pandemic. Rapidly and widely adopting a proposal by the National Academies of Science, Engineering and Medicine's (NASEM), a majority of US states (n=34) addressed SRD by including disadvantage indices (DIs) in vaccine allocation plans. DIs are place- based statistical measures of deprivation or vulnerability that integrate Census data such as income, education or quality of housing, to rank geographic areas as small as neighborhoods. Because one of the consequences of SRD is that people of color face reduced economic and housing opportunities and account for larger shares of disadvantaged communities, DIs simultaneously capture SRD impact, and offer tools for mitigation. For example, under severe scarcity, DIs were used to increase vaccine shares for disadvantaged areas, and, by extension, more people of color. DIs hence mitigated the risk that traditional allocation frameworks result in SRD, even if unintended. Still, the rapid adoption and wide rangeof uses leave unclear what the optimal uses of DIs are within and outside of health emergencies. Our goal is to determine the strengths and weaknesses of DIs in addressing SRD in Covid-19, future pandemics, public health and clinical care. As mixed-methods s DIs disadvantaged alongside incidence, difference-in-difference on with weaknesses SRD the hospital will disadvantaged a highly interdisciplinary team collaborating with a community advisory board, we propose an observational tudy with 2 aims. First, we will identify the impact, strengths, and weaknesses of using in Covid-19 vaccine allocation to address SRD and improve healthcare access and outcomes of communities of color : We will evaluate t he impact of the 3 most frequently used DIs a newly launched CDC/HHS index (the Minority Health Social Vulnerability Index ) on Covid-19 hospitalizations, deaths and vaccination rates by race and ethnicity, using predictive modeling and analyses of states' actual vaccine-roll-out. We will also conduct ualitative interviews facilitators and barriers of DIs with vaccine allocation and health equity leaders in the 32 CDC jurisdictions the largest shares of disadvantaged communities. Second, we wil identify the possible strengths and of using DIs in public health and clinical care outside of emergency settings to address and improve healthcare access and outcomes of disadvantaged communities of color: We will use Delphi method to identify how health department equity taskforce leaders, and equity leaders in the largest systems in the same 32 CDC jurisdictions, rank concrete uses of DIs, identified from the literature. We complement expert views with two innovative nationally representative survey-experiments, and engaging communities in impactful award-winning group deliberation (CHoosing All Together-CHAT) q l .", "keywords": [ "Address", "Adopted", "Adoption", "Advisory Committees", "Area", "Award", "COVID-19", "COVID-19 vaccine", "Censuses", "Centers for Disease Control and Prevention (U.S.)", "Cessation of life", "Color", "Communicable Diseases", "Communication", "Communities", "Complement", "Data", "Disadvantaged", "Discrimination", "Economics", "Education", "Emergency Situation", "Engineering", "Ethics", "Ethnic Origin", "Ethnic group", "Evaluation", "Exercise", "Face", "Future", "Geographic Locations", "Geographic state", "Goals", "Health", "Health Services Research", "Health system", "Healthcare", "Hospitalization", "Hospitals", "Housing", "Incidence", "Income", "Interview", "Journals", "Knowledge", "Laws", "Life Expectancy", "Literature", "Measures", "Medicine", "Methods", "Modeling", "Neighborhoods", "Outcome", "Persons", "Play", "Psychology", "Public Health", "Qualitative Methods", "Race", "Recommendation", "Recording of previous events", "Research", "Resources", "Review Literature", "Risk", "Role", "SARS-CoV-2 exposure", "Shapes", "Site", "Structural Racism", "Surveys", "System", "Time", "United States National Academy of Sciences", "Vaccination", "Vaccines", "Work", "base", "care outcomes", "clinical care", "community partnership", "deprivation", "design", "disadvantaged population", "disorder control", "disparity reduction", "emergency settings", "experimental study", "health care availability", "health care disparity", "health difference", "health equity", "health equity promotion", "health outcome disparity", "improved", "indexing", "innovation", "minority health", "novel", "outreach", "pandemic disease", "people of color", "predictive modeling", "preference", "racism", "social", "social vulnerability", "symposium", "tool", "vaccine distribution" ], "approved": true } }, { "type": "Grant", "id": "12040", "attributes": { "award_id": "5R01AI170137-02", "title": "Using Disadvantage Indices to Address Structural Racism and Discrimination in Pandemic Vaccine Allocation and Beyond: Defining the Shape of a Novel Paradigm to Promote Health Equity", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 6243, "first_name": "BROOKE ALLISON", "last_name": "Bozick", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2027-06-30", "award_amount": 782973, "principal_investigator": { "id": 25578, "first_name": "Thomas Harald", "last_name": "Schmidt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25579, "first_name": "Ruqaiijah", "last_name": "Yearby", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Covid-19 exposed inequitable healthcare access and disparate health outcomes of people of color, that are due to structural racism and discrimination (SRD)—but in an unprecedented turn, policymakers also deployed a major novel tool to address SRD within, and likely outside of the pandemic. Rapidly and widely adopting a proposal by the National Academies of Science, Engineering and Medicine's (NASEM), a majority of US states (n=34) addressed SRD by including disadvantage indices (DIs) in vaccine allocation plans. DIs are place- based statistical measures of deprivation or vulnerability that integrate Census data such as income, education or quality of housing, to rank geographic areas as small as neighborhoods. Because one of the consequences of SRD is that people of color face reduced economic and housing opportunities and account for larger shares of disadvantaged communities, DIs simultaneously capture SRD impact, and offer tools for mitigation. For example, under severe scarcity, DIs were used to increase vaccine shares for disadvantaged areas, and, by extension, more people of color. DIs hence mitigated the risk that traditional allocation frameworks result in SRD, even if unintended. Still, the rapid adoption and wide rangeof uses leave unclear what the optimal uses of DIs are within and outside of health emergencies. Our goal is to determine the strengths and weaknesses of DIs in addressing SRD in Covid-19, future pandemics, public health and clinical care. As mixed-methods s DIs disadvantaged alongside incidence, difference-in-difference on with weaknesses SRD the hospital will disadvantaged a highly interdisciplinary team collaborating with a community advisory board, we propose an observational tudy with 2 aims. First, we will identify the impact, strengths, and weaknesses of using in Covid-19 vaccine allocation to address SRD and improve healthcare access and outcomes of communities of color : We will evaluate t he impact of the 3 most frequently used DIs a newly launched CDC/HHS index (the Minority Health Social Vulnerability Index ) on Covid-19 hospitalizations, deaths and vaccination rates by race and ethnicity, using predictive modeling and analyses of states' actual vaccine-roll-out. We will also conduct ualitative interviews facilitators and barriers of DIs with vaccine allocation and health equity leaders in the 32 CDC jurisdictions the largest shares of disadvantaged communities. Second, we wil identify the possible strengths and of using DIs in public health and clinical care outside of emergency settings to address and improve healthcare access and outcomes of disadvantaged communities of color: We will use Delphi method to identify how health department equity taskforce leaders, and equity leaders in the largest systems in the same 32 CDC jurisdictions, rank concrete uses of DIs, identified from the literature. We complement expert views with two innovative nationally representative survey-experiments, and engaging communities in impactful award-winning group deliberation (CHoosing All Together-CHAT) q l .", "keywords": [ "Address", "Adopted", "Adoption", "Advisory Committees", "Area", "Award", "COVID-19", "COVID-19 vaccine", "Censuses", "Cessation of life", "Collaborations", "Color", "Communicable Diseases", "Communication", "Communities", "Complement", "Data", "Disadvantaged", "Discrimination", "Disparity population", "Economics", "Education", "Emergency Situation", "Engineering", "Equity", "Ethics", "Ethnic Origin", "Ethnic Population", "Evaluation", "Exercise", "Face", "Geographic Locations", "Goals", "Health", "Health Services Research", "Health system", "Healthcare", "Hospitalization", "Hospitals", "Housing", "Incidence", "Income", "Inequity", "Interview", "Journals", "Knowledge", "Laws", "Learning", "Life Expectancy", "Literature", "Measures", "Medicine", "Methods", "Modeling", "Neighborhoods", "Outcome", "Persons", "Play", "Policy Maker", "Psychology", "Public Health", "Qualifying", "Qualitative Methods", "Race", "Recommendation", "Recording of previous events", "Research", "Resources", "Review Literature", "Role", "SARS-CoV-2 exposure", "Shapes", "Site", "Structural Racism", "Surveys", "System", "Time", "US State", "United States National Academy of Sciences", "Vaccination", "Vaccines", "Work", "care outcomes", "clinical care", "community engagement", "community partnership", "deprivation", "design", "disorder control", "disparity reduction", "emergency settings", "experimental study", "future pandemic", "health care availability", "health care disparity", "health difference", "health equity", "health equity promotion", "health outcome disparity", "improved", "indexing", "innovation", "minority health", "neighborhood disadvantage", "novel", "outreach", "pandemic disease", "people of color", "predictive modeling", "preference", "racial population", "racism", "risk mitigation", "social", "social vulnerability", "symposium", "tool", "vaccine distribution" ], "approved": true } }, { "type": "Grant", "id": "10626", "attributes": { "award_id": "1R01GM147635-01", "title": "Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions", "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": 23658, "first_name": "FRANK PAUL", "last_name": "Shewmaker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-20", "end_date": "2026-08-31", "award_amount": 424111, "principal_investigator": { "id": 26671, "first_name": "Sefika Banu", "last_name": "Ozkan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26672, "first_name": "LISKIN", "last_name": "SWINT-KRUSE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 912, "ror": "", "name": "ARIZONA STATE UNIVERSITY-TEMPE CAMPUS", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Advanced sequencing technologies provide ever-increasing quantities of data about human genetic variation and viral evolution. However, predicting the outcomes of missense mutations in protein coding regions remains a challenge, creating a bottleneck in discriminating biomedically-relevant variants from neutral ones (with little or no effect on phenotype). In particular, outcome predictions are very poor when a missense mutation alters amino acids that are located far from a protein’s functional/binding sites. These shortcomings also impair protein design. We propose to ameliorate these needs by developing quantitative, computational models that predict the effects of long-distance substitutions on binding interactions. To that end, we have developed an approach in which (1) a protein’s collective motions are first revealed by molecular dynamics simulations and then (2) force perturbation is used to disrupt the protein’s equilibrium, thereby approximating the effects of ligand binding. We have used this approach in published studies and preliminary data to illuminate the propagation of dynamical changes through a protein’s anisotropic network of interactions. Results suggest that changes in these dynamic networks have crucial effects on protein function, thereby leading to our central hypothesis: The effects of long- distance substitutions on ligand binding are emergent properties of changes in the protein’s dynamically-coupled, anisotropic network. The goal of the current proposal is to extend this computational approach to develop models that predict: (Aim 1) the magnitudes of binding affinity changes arising from long-distance, modulating substitutions; (Aim 2) which pairs of non-contact substitutions have non-additive effects on binding affinities (“epistasis”); and (Aim 3) which long-distance positions contribute to ligand specificity. To that end, we have a well-established collaboration that allows us to iterate between computational predictions and experimental testing, enabling development of quantitative models with computed accuracies. Our preliminary studies used the well-characterized E. coli lactose repressor protein (LacI), for which experimental results validate our preliminary computational models and provide specific hypotheses for Aims 1-3. Additional model proteins will be used to show the generality of our approach and will include the LacI homolog PurR, the cAMP receptor protein, and a viral protease SARS-Cov2-Mpro. Results will be used to provide novel computational tools for predicting functional outcomes of long-distance substitutions. The success of this project will catalyze research at the interface of protein structural biology, molecular genetics, evolution and medicine by advancing the mechanistic understanding of how substitutions distal from functional sites alter ligand binding.", "keywords": [ "3-Dimensional", "Affinity", "Algorithms", "Amino Acid Substitution", "Amino Acids", "Automobile Driving", "Binding", "Binding Sites", "Biological", "Biophysics", "Collaborations", "Computer Models", "Computers", "Coupled", "Coupling", "Cyclic AMP Receptor Protein", "DNA Binding", "Data", "Development", "Disease", "Distal", "Equilibrium", "Escherichia coli", "Event", "Evolution", "Exhibits", "Genetic Epistasis", "Genetic Variation", "Goals", "Homologous Protein", "Human Genetics", "Impairment", "Lactose", "Ligand Binding", "Ligands", "Machine Learning", "Measures", "Medicine", "Methods", "Missense Mutation", "Modeling", "Molecular", "Molecular Genetics", "Motion", "Mutation", "Open Reading Frames", "Outcome", "Phenotype", "Positioning Attribute", "Property", "Protein Dynamics", "Protein Engineering", "Proteins", "Publishing", "Recording of previous events", "Repressor Proteins", "Research", "Rest", "SARS-CoV-2 protease", "SARS-CoV-2 spike protein", "Sampling", "Site", "Specificity", "System", "Technology", "Testing", "Variant", "Viral", "Viral Proteins", "base", "combinatorial", "computerized tools", "delta protein", "experimental study", "flexibility", "functional outcomes", "genetic evolution", "improved", "molecular dynamics", "molecular modeling", "network models", "novel", "outcome prediction", "personalized medicine", "predictive modeling", "predictive tools", "protein function", "response", "structural biology", "success" ], "approved": true } } ], "meta": { "pagination": { "page": 1392, "pages": 1424, "count": 14236 } } }