Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=-id
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-id", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-id", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-id", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=-id" }, "data": [ { "type": "Grant", "id": "733", "attributes": { "award_id": "2042715", "title": "Planning a Shared Autonomous Vehicle Mobility Pilot for Linking Affordable Housing and Jobs", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1715, "first_name": "David", "last_name": "Corman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2022-06-30", "award_amount": 49693, "principal_investigator": { "id": 1717, "first_name": "Levent", "last_name": "Guvenc", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 308, "ror": "", "name": "Ohio State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1716, "first_name": "Bilin", "last_name": "Aksun-Guvenc", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 308, "ror": "", "name": "Ohio State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "Low-mid income residents living in affordable housing areas of a city are often faced with problems in accessing jobs or other locations of opportunity due to limited mobility choices including the lack of a public transportation system. Since the underlying problem is usually the high and therefore unfeasible cost of operating public transportation or other similar mobility options, operation of a fleet of ride hailing and ride-sharing autonomous vehicles that can also be used for the delivery of food and supplies during a pandemic like the current Covid-19 outbreak is proposed as a feasible solution. This approach of deploying ride Shared Autonomous Vehicles (SAV) in a transit desert will give residents an on-demand, fast and affordable option of connecting to their job locations, allowing them to have faster and reliable access to work, services and amenities that are otherwise not so easily accessible in reasonable travel periods. The broader impact of this project is to benefit society and advance the desired societal outcome of improving access to jobs from low-mid income neighborhoods in a smart city. The team will be addressing these needs from both the technological perspective of SAVs as well as from the social science dimension of this technology can both work with the community and be shaped by it.This project brings together researchers from several disciplines with community partners to plan an affordable, innovative mobility solution that better connects residents to work, critical services, and amenities. The research pilot deployment that will be planned in this project is the use of SAVs in the City of Marysville, Ohio to give its residents a fast and affordable option of connecting to their job locations. The SAVs will be fully electric vehicles with an optimal routing algorithm that will reduce their trip time and energy footprint. The project team will identify demand locations corresponding to affordable housing in Marysville and destination locations corresponding to jobs, determine the geo-fenced area of SAV operation, plan integration with the existing transportation systems for increasing trip range, analyze existing traffic data, build an SAV operation simulation environment for the chosen geo-fenced area and use it to plan a research pilot deployment. The project team will also identify vendors and methods for operation of the pilot. The planning work of this Stage 1 planning project will be compiled into a Stage 2 proposal and will include the plan for the Stage 2 research pilot and how it will be evaluated for success. This project is funded in response to CIVIC Innovation Challenge Track A. Communities and Mobility: Offering Better Mobility Options to Solve the Spatial Mismatch Between Housing Affordability and Jobs which is a collaboration between NSF and Department of Energy Vehicle Technology Office.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": "732", "attributes": { "award_id": "2115126", "title": "Collaborative Research: National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1713, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-01", "end_date": "2021-09-30", "award_amount": 66071, "principal_investigator": { "id": 1714, "first_name": "B Aditya", "last_name": "Prakash", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "In the past year, the ongoing COVID-19 pandemic has severely disrupted the livelihoods of our planet’s human inhabitants, infecting over 85 million individuals, and causing nearly 2 million deaths. What actions might have been taken to minimize the severity of this pandemic (and others before it in the past decades such as Zika, SARS and Ebola)? In retrospect, many actions could have played key roles: environmental monitoring for potential animal-to-human infection spillovers, establishment of pipelines for rapid vaccine development and optimal deployment and distribution, designing data-science tools to accurately forecast trajectories, fast and adaptive syndromic surveillance and behavior tracking, designing and timing effective interventions, training susceptible individuals for measures needed to inhibit the spread of infectious agents, and others. What lessons have been learned and what gaps in our knowledge, methodologies, technologies, and policies remain? The investigators propose a two-day multi-disciplinary National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT) to begin to address these and related challenges. As a whole the highly interdisciplinary organizing team has significant experience in various aspects of the topics touched upon by this symposium. Bridging fundamental gaps in what is known (and perhaps even what is knowable) can require coordination that goes far beyond sharing of instruments, standardization, or the exchange of methods and data; these define broader societal challenges of complex problems beyond pandemic prediction. This meeting will help enable coordinated team-science efforts that can assist in bringing disparate groups together, whether in small teams or large teams, including bringing in the public as citizen scientists.Key in fostering convergence for predictive intelligence for pandemic prevention will be co-envisioning computing, science and engineering in ways that are integrated across disciplines so that community efforts are optimally suited to (and nimbly able to) respond to and prevent new pandemics. The symposium has been structured around four themes and perspectives: Molecular, Physiological, Population/Epidemiological and End-end/Multi-scale. The proposed meeting will provide a valuable opportunity for the community to begin to build the necessary convergence. A combination of plenary talks, short talks, panel discussions and small breakout thought sessions will be used to help achieve these aims. For several significant reasons, predictive intelligence for pandemic prevention stands to benefit by drawing upon convergent computation, science and engineering insights alongside traditional disciplinary repositories of expertise.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": "731", "attributes": { "award_id": "2115122", "title": "Collaborative Research: National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1711, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-01", "end_date": "2021-09-30", "award_amount": 26422, "principal_investigator": { "id": 1712, "first_name": "Paul M", "last_name": "Torrens", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 167, "ror": "https://ror.org/0190ak572", "name": "New York University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "In the past year, the ongoing COVID-19 pandemic has severely disrupted the livelihoods of our planet’s human inhabitants, infecting over 85 million individuals, and causing nearly 2 million deaths. What actions might have been taken to minimize the severity of this pandemic (and others before it in the past decades such as Zika, SARS and Ebola)? In retrospect, many actions could have played key roles: environmental monitoring for potential animal-to-human infection spillovers, establishment of pipelines for rapid vaccine development and optimal deployment and distribution, designing data-science tools to accurately forecast trajectories, fast and adaptive syndromic surveillance and behavior tracking, designing and timing effective interventions, training susceptible individuals for measures needed to inhibit the spread of infectious agents, and others. What lessons have been learned and what gaps in our knowledge, methodologies, technologies, and policies remain? The investigators propose a two-day multi-disciplinary National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT) to begin to address these and related challenges. As a whole the highly interdisciplinary organizing team has significant experience in various aspects of the topics touched upon by this symposium. Bridging fundamental gaps in what is known (and perhaps even what is knowable) can require coordination that goes far beyond sharing of instruments, standardization, or the exchange of methods and data; these define broader societal challenges of complex problems beyond pandemic prediction. This meeting will help enable coordinated team-science efforts that can assist in bringing disparate groups together, whether in small teams or large teams, including bringing in the public as citizen scientists.Key in fostering convergence for predictive intelligence for pandemic prevention will be co-envisioning computing, science and engineering in ways that are integrated across disciplines so that community efforts are optimally suited to (and nimbly able to) respond to and prevent new pandemics. The symposium has been structured around four themes and perspectives: Molecular, Physiological, Population/Epidemiological and End-end/Multi-scale. The proposed meeting will provide a valuable opportunity for the community to begin to build the necessary convergence. A combination of plenary talks, short talks, panel discussions and small breakout thought sessions will be used to help achieve these aims. For several significant reasons, predictive intelligence for pandemic prevention stands to benefit by drawing upon convergent computation, science and engineering insights alongside traditional disciplinary repositories of expertise.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": "730", "attributes": { "award_id": "2043611", "title": "SCC-CIVIC-PG Track A: Leveraging AI-assist Microtransit to Ameliorate Spatiotemporal Mismatch between Housing and Employment", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1706, "first_name": "Linda", "last_name": "Bushnell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2021-12-31", "award_amount": 49898, "principal_investigator": { "id": 1710, "first_name": "Dongxiao", "last_name": "Zhu", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 179, "ror": "https://ror.org/01070mq45", "name": "Wayne State University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1707, "first_name": "Daniel", "last_name": "Grosu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1708, "first_name": "Tierra", "last_name": "Bills", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1709, "first_name": "Marco", "last_name": "Brocanelli", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 179, "ror": "https://ror.org/01070mq45", "name": "Wayne State University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 disproportionately affects the low-wage workers whose spatiotemporal mobility pattens, e.g., between housing and job, have dramatically changed. Microtransit service has been recently launched in Detroit to complement the existing public transit options. Despite the initial success, a salient issue is how to effectively and efficiently utilize microtransit resources to ameliorate spatiotemporal mismatch between employment and housing for low-wage workers. With the rise of Artificial Intelligence (AI) and increasingly available smart mobility data, the vision of this research project is to create a dynamic routing prediction system based on learning the hourly mobility patterns between jobs and housing. It is designed for the stakeholders (i.e., community advocates and public transport authority) to visualize and forecast the mismatch between employment and housing, which is translated into a dynamic trip demand that can be used to design adaptive routing algorithms to optimize the allocation of microtransit resources and to enhance micromobility via minimizing the rider’s first/last mile. Currently public transportation with fixed routes and schedules are periodically tweaked and/or augmented to ameliorate the ever-changing spatial mismatch. Despite its long-term effectiveness, it is not sufficiently flexible to adapt to the hourly spatiotemporal variation of jobs-housing mobility patterns primarily from the hourly paid workers. The long-term goal of this project is to work with civic partners in the city of Detroit to (1) design, implement and deploy an AI-assist microtransit system to ameliorate the spatiotemporal mismatch between housing and employment, particularly for the low-wage workers residing in the under resourced neighborhoods; and (2) use geocoded socioeconomic data to identify the community with disparities in mobility and deploy smart mobility technology to reduce the disparities and foster thriving communities. The project’s near-term objective is to leverage and power the existing microtransit service with cutting-edge technology and select a few spatiotemporally mismatched regions in Detroit as the testbed for our smart mobility strategy.The research innovation is expected to provide immediate, low-cost yet effective public transit solutions that are expected to bring an immediate benefit to the vulnerable communities in Detroit by significantly reducing transit risk, commute time/distance and trip cost. It can be replicated to other US cities to ameliorate the spatiotemporal mismatch between housing and employment. In addition, it can provide insight for designing long-term intervention strategies to eliminate the mismatch and reduce the mobility disparities, for example, government to launch new transportation options and create jobs; and builders to develop housing in the mismatched regions.This project is in response to Track A – CIVIC Innovation Challenge - Communities and Mobility a collaboration with NSF and the Department of Energy.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": "729", "attributes": { "award_id": "2115300", "title": "Collaborative Research: National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1704, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-01", "end_date": "2021-09-30", "award_amount": 11378, "principal_investigator": { "id": 1705, "first_name": "Krista R", "last_name": "Wigginton", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "In the past year, the ongoing COVID-19 pandemic has severely disrupted the livelihoods of our planet’s human inhabitants, infecting over 85 million individuals, and causing nearly 2 million deaths. What actions might have been taken to minimize the severity of this pandemic (and others before it in the past decades such as Zika, SARS and Ebola)? In retrospect, many actions could have played key roles: environmental monitoring for potential animal-to-human infection spillovers, establishment of pipelines for rapid vaccine development and optimal deployment and distribution, designing data-science tools to accurately forecast trajectories, fast and adaptive syndromic surveillance and behavior tracking, designing and timing effective interventions, training susceptible individuals for measures needed to inhibit the spread of infectious agents, and others. What lessons have been learned and what gaps in our knowledge, methodologies, technologies, and policies remain? The investigators propose a two-day multi-disciplinary National Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT) to begin to address these and related challenges. As a whole the highly interdisciplinary organizing team has significant experience in various aspects of the topics touched upon by this symposium. Bridging fundamental gaps in what is known (and perhaps even what is knowable) can require coordination that goes far beyond sharing of instruments, standardization, or the exchange of methods and data; these define broader societal challenges of complex problems beyond pandemic prediction. This meeting will help enable coordinated team-science efforts that can assist in bringing disparate groups together, whether in small teams or large teams, including bringing in the public as citizen scientists.Key in fostering convergence for predictive intelligence for pandemic prevention will be co-envisioning computing, science and engineering in ways that are integrated across disciplines so that community efforts are optimally suited to (and nimbly able to) respond to and prevent new pandemics. The symposium has been structured around four themes and perspectives: Molecular, Physiological, Population/Epidemiological and End-end/Multi-scale. The proposed meeting will provide a valuable opportunity for the community to begin to build the necessary convergence. A combination of plenary talks, short talks, panel discussions and small breakout thought sessions will be used to help achieve these aims. For several significant reasons, predictive intelligence for pandemic prevention stands to benefit by drawing upon convergent computation, science and engineering insights alongside traditional disciplinary repositories of expertise.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": "728", "attributes": { "award_id": "2043875", "title": "Collaborative Research: Student engagement with online formative assessments: Identifying access and barriers to resource use at two-year and four-year institutions", "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": 1701, "first_name": "Kalyn", "last_name": "Owens", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2024-07-31", "award_amount": 49198, "principal_investigator": { "id": 1703, "first_name": "Gabrielle B", "last_name": "Johnson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 378, "ror": "https://ror.org/004gw9825", "name": "Southeast Community College", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1702, "first_name": "Sarah K", "last_name": "Spier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 378, "ror": "https://ror.org/004gw9825", "name": "Southeast Community College", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by improving student engagement in undergraduate biology courses at two-year and four-year institutions. Specifically, the research team will explore how students interact with online assignments and what factors influence that interaction. Formative assessments give students the opportunity to test and revise their understanding of course content before their learning is measured to determine their course grade. However, instructors often expect students to complete formative assessments outside of class, typically via online platforms. The expectation of this additional work creates challenges for some students. For example, students may have limited access to technology or have external responsibilities that affect the time they can spend on these assignments. Such challenges and their unequal distribution have been spotlighted because of the rapid and sustained increase in higher education’s dependence on remote or online learning in response to the COVID-19 pandemic. It is likely that these challenges affect how students approach learning and the resources they choose. However, few studies have been done to explore student approaches to completing online assignments. This project aims to help fill this gap, and thus provide tools to improve students’ instructional strategies and enhance their learning in online environments. This project intends to benefit society by providing insights into how student backgrounds and circumstances affect their learning behaviors in online courses and how these behaviors affect course outcomes.Building on prior research, this project will adopt a multi-methods approach to investigate and improve undergraduate student engagement with online formative assessments completed outside of class time. The project team will pioneer a novel two-stage protocol involving video recordings of biology students completing authentic online course assignments and then conduct follow-up interviews to clarify their learning approaches, discuss why they used certain resources (e.g., classmates; textbooks; websites), and identify potential barriers to resource access (e.g., internet availability; social connectedness). Drawing from these interviews, the project team will create closed-ended surveys for students to report their learning approaches and resource use and indicate external factors that might affect their ability to complete online assignments. Administering these instruments broadly will allow exploration of quantitative connections between personal demographics, access/barriers, formative assessments utilization behaviors, and learning outcomes. With particular attention to the potential barriers faced by underserved groups, the project team will facilitate a working group of biology faculty that will develop and implement strategies to guide students about how to overcome challenges and interact productively with online assignments. The newly developed survey instruments will be used to monitor the impact that this guidance has on student outcomes. By disseminating findings through reports to educational communities, this project will help replace anecdotal intuitions with evidence-based accounts of student experiences in completing online assignments. Investigating asynchronous assignment completion represents a critical research direction given the longstanding desire among institutions to develop flexible course formats that work with busy student schedules. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This Level 1 project is aligned with the Engaged Student Learning track, which the supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "727", "attributes": { "award_id": "2044243", "title": "Collaborative Research: Student Engagement with Online Formative Assessments: Identifying Access and Barriers to Resource Use by Students at Two-year and Four-year Institutions", "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": 1697, "first_name": "Kalyn", "last_name": "Owens", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2024-07-31", "award_amount": 250724, "principal_investigator": { "id": 1700, "first_name": "Brian A", "last_name": "Couch", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 298, "ror": "", "name": "University of Nebraska-Lincoln", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1698, "first_name": "Kathleen R", "last_name": "Brazeal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1699, "first_name": "Lorey", "last_name": "Wheeler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 298, "ror": "", "name": "University of Nebraska-Lincoln", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by improving student engagement in undergraduate biology courses at two-year and four-year institutions. Specifically, the research team will explore how students interact with online assignments and what factors influence that interaction. Formative assessments give students the opportunity to test and revise their understanding of course content before their learning is measured to determine their course grade. However, instructors often expect students to complete formative assessments outside of class, typically via online platforms. The expectation of this additional work creates challenges for some students. For example, students may have limited access to technology or have external responsibilities that affect the time they can spend on these assignments. Such challenges and their unequal distribution have been spotlighted because of the rapid and sustained increase in higher education’s dependence on remote or online learning in response to the COVID-19 pandemic. It is likely that these challenges affect how students approach learning and the resources they choose. However, few studies have been done to explore student approaches to completing online assignments. This project aims to help fill this gap, and thus provide tools to improve students’ instructional strategies and enhance their learning in online environments. This project intends to benefit society by providing insights into how student backgrounds and circumstances affect their learning behaviors in online courses and how these behaviors affect course outcomes.Building on prior research, this project will adopt a multi-methods approach to investigate and improve undergraduate student engagement with online formative assessments completed outside of class time. The project team will pioneer a novel two-stage protocol involving video recordings of biology students completing authentic online course assignments and then conduct follow-up interviews to clarify their learning approaches, discuss why they used certain resources (e.g., classmates; textbooks; websites), and identify potential barriers to resource access (e.g., internet availability; social connectedness). Drawing from these interviews, the project team will create closed-ended surveys for students to report their learning approaches and resource use and indicate external factors that might affect their ability to complete online assignments. Administering these instruments broadly will allow exploration of quantitative connections between personal demographics, access/barriers, formative assessments utilization behaviors, and learning outcomes. With particular attention to the potential barriers faced by underserved groups, the project team will facilitate a working group of biology faculty that will develop and implement strategies to guide students about how to overcome challenges and interact productively with online assignments. The newly developed survey instruments will be used to monitor the impact that this guidance has on student outcomes. By disseminating findings through reports to educational communities, this project will help replace anecdotal intuitions with evidence-based accounts of student experiences in completing online assignments. Investigating asynchronous assignment completion represents a critical research direction given the longstanding desire among institutions to develop flexible course formats that work with busy student schedules. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This Level 1 project is aligned with the Engaged Student Learning track, which the supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "726", "attributes": { "award_id": "2032521", "title": "Collaborative Research: Three-Dimensional Flexible Biosensor Enabling Label-Free Spatial Mapping of Intra-Organoid Functions", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1695, "first_name": "Aleksandr", "last_name": "Simonian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2024-01-31", "award_amount": 320000, "principal_investigator": { "id": 1696, "first_name": "Hyunjoon", "last_name": "Kong", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Over the last decades, biologists and bioengineers have attempted to engineer \"living\" cell masses that exhibit tissues and organ structure and function outside of the body. These \"living\" cell masses, named organoids, can serve as a low-cost, rapid, but precise drug-screening platform and, ultimately, replace animal models. Despite the promise, there remains a lack of tools to enable real-time, non-invasive monitoring of the organoids' biological activities. This project supports an integrated research and educational program with goals to (1) develop a three-dimensional flexible sensor deciphering electrophysiological actions of human heart-like organoids and use it to predict the effect of potential anti-viral drugs for COVID-19 on the human heart, and (2) develop a multidisciplinary educational framework associated with the biosensor for multiple levels of students, especially from traditionally underrepresented groups in science and engineering. The proposed research will make a positive and immediate impact on U.S. health and economy by providing a novel organoid-sensor platform useful to determine powerful therapeutics to the on-going COVID-19 and future, unforeseeable outbreak.For the last decades, extensive efforts have been made to recapitulate the multicellular, anatomical, and functional hallmarks of organs, thereby offering comprehensive frameworks to model organ development, homeostasis, regeneration, and disease. These movements have quickly engineered a variety of organ-like multicellular clusters named organoids. However, there remains a lack of tools enabling label-free, real-time, and non-invasive monitoring of intra-organoid functions. This project aims to establish a set of materials, design layouts, and assembly methods to develop a three-dimensional flexible intra-organoid sensor instrumented with vertically ordered silicon nanoneedles. As a model system, this sensor will be tailored for label-free spatial mapping of electrocardiogram signals from the inside of cardiovascular organoids that are engineered by orchestrating spatially-organized co-differentiation of pluripotent stem cells to cardiac muscle cells and endothelial cells. The quantitative readout of the intra-organoid activities will facilitate improved understanding of the underlying anatomical-electrophysiological-mechanical relationships of cardiac function. Furthermore, this intra-organoid sensor platform will become a transformative organ-on-a-chip tool that will greatly assist efforts to determine the efficacy of newly developed drugs as well as the impact of unidentified toxins. Complementary experimental and computational methods will be established for analyzing time-series data associated with vascularized cardiac muscle functions. This collaborative research has been built upon a strong research tie of the Multiple-PIs in joint efforts over the past 2 years based on a long-standing relationship between Purdue University and the University of Illinois at Urbana-Champaign. Because the universities are within close geographic proximity in the Midwest, the investigators at Purdue University will be able to spend significant amounts of time in the clinical setting at the University of Illinois at Urbana-Champaign in order to not only obtain timely feedback from the clinical perspectives but also ensure the progress of the proposed tasks.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": "725", "attributes": { "award_id": "2049782", "title": "Doctoral Dissertation Research in DRMS: Building a comprehensive understanding of enterprise risks and their interdependencies for improved risk-intelligence", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1692, "first_name": "Claudia", "last_name": "Gonzalez-Vallejo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2021-12-31", "award_amount": 37975, "principal_investigator": { "id": 1694, "first_name": "Joseph V", "last_name": "Sinfield", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1693, "first_name": "Ananya B", "last_name": "Sheth", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Businesses are ubiquitous and inseparably merged with human lives. Thriving corporations provide the fundamental activity necessary for an equitable society with benefits ranging from the ready availability of goods and services to regional employment in support of economic prosperity. Simultaneously, high-risk events such as COVID‐19 are bleak reminders that enterprises are continuously threatened, and that building enterprise resilience is crucial. Yet, one of the cornerstones of enterprise resilience - risk intelligence – or the adequate awareness of the various risk factors and their complex interdependence, remains grossly underdeveloped. This project addresses this gap in knowledge by developing complex network views of enterprise risks. Employing big data and tools of artificial intelligence the project identifies and develops an enterprise-agnostic risk inventory that is considerably more comprehensive than any such publicly available resource. In addition, expert input allows for converting seemingly disconnected risk factors into interconnected complex risk networks, which enable the search for risk chains that may compound and lead to more significant adverse effects. This work builds a knowledge resource base useful to explain mechanisms of cascading risks and to predict the varying impacts of risk events on enterprises. Thus, the work serves national interest and is in alignment with NSF’s mission to promote the progress of science, and via that, advance national prosperity and welfare.This work is grounded in the complexity systems view of enterprise risk management and seeks to build a comprehensive, data‐informed view of the dynamic risk network influencing enterprises to systematically enhance risk awareness and contribute toward the evolution of truly risk‐intelligent organizations. This perspective is achieved through: a) the development of a comprehensive enterprise agnostic risk factor inventory, b) the generation of risk networks that map risk factor interrelations, and c) the exploration of the complex dynamics of these risk networks. The work entails a mixed-methods approach utilizing information extraction (IE) on a large, curated dataset of company risks, and Fuzzy Cognitive Mapping (FCM) for complex risk-network development and analyses. Public information (SEC filings) are augmented with private risk data (analyst reports) for enterprises in the S&P 500, providing robust coverage of true risk factors. The corpus is analyzed using IE principles, which include part‐of‐speech tagging, dependency-parsing, n‐gram extraction, and topic modeling. Surveyed and/or interviewed experts from industry and academia inform qualitative measures of risk interaction (dependencies, direction, and degree of influence) during the FCM process leading to complex risk network development. The resulting risk networks are analyzed quantitatively to reveal insights such as centrality of risks, the distances between risks, and sub‐group structures within the risk networks that could inform an order of critical risks. The research contributes to the field of Enterprise Risk Management (ERM) by increasing scholarly awareness on the breadth of risks affecting enterprises. Further, via FCM, the work converts expert understanding into a quantifiable network, bringing focus on risk interdependencies. In addition, via network analysis, the effort illuminates critical risks and propagation mechanisms that may be overlooked in traditional views. Overall, this effort provides an expansive, data‐informed view of risk factors affecting enterprises, their (non-intuitive) interactions, and related dynamics thereby advancing the complexity-view of ERM research as well as sharpening an enterprise’s ability to predict cascading effects caused by seemingly unrelated events.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": "724", "attributes": { "award_id": "2043455", "title": "SCC-CIVIC-PG Track B: Innovation for Economic Rejuvenation of Louisiana Coastal Communities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1687, "first_name": "Linda", "last_name": "Bushnell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2021-06-30", "award_amount": 50000, "principal_investigator": { "id": 1691, "first_name": "Mira S", "last_name": "Olson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1688, "first_name": "Shirley B", "last_name": "Laska", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1689, "first_name": "Franco A", "last_name": "Montalto", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, { "id": 1690, "first_name": "Kristina J", "last_name": "Peterson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "The long-term vision of this project is a sustainable, generative and adaptive regional economy for coastal Louisiana, a “working coast” that is both economically and environmentally susceptible to negative impacts from climate change and human-induced disasters. The focus is to develop economic activity that will support resiliency in areas experiencing disasters. Communities in coastal Louisiana have suffered crippling and repeated damage from hurricanes, coastal sea-level rise, offshore oil spills, and channelization of marshes for oil and gas exploration, production and distribution, and are increasingly vulnerable to Atlantic hurricanes with Gulf water warming. Within this complex context of risks, COVID-19 has also threatened the health of coastal community residents and forced layoffs of hundreds of coastal and support industry workers employed by the offshore oil and gas industry. A key element in the transition to new, greener industries and occupations is building on and adapting existing skills and infrastructure, and transferring these skills to the next generation. This project engages twelve diverse communities along the Louisiana coast in a community-based participatory action research (CBPAR) process to generate and select ideas for economic activities, products and services that can be produced by the existing workforce while preserving the environmental integrity of the coast and its cultures. The proposed project advances fundamental understanding of how to align skills and knowledge development for personnel trained in the offshore oil and gas exploration/production industry using a partnered approach to generating and assimilating knowledge from diverse stakeholders to create sustainable economic choices and a diverse economic income structure for more resilient livelihoods, community stability and disaster resiliency. The primary objectives of this planning grant are: (1) to create two inventories, one of existing skills and interests of coastal personnel and one of available infrastructure and equipment that may have value in a green economy; (2) to perform a gap analysis of regional economic opportunities and green industries and their inland markets; (3) to innovate an economic rejuvenation plan for Louisiana coastal communities through a community-based participatory action research (CBPAR) workshop that applies data collected with the communities; (4) to establish federally-funded work study partnerships with coastal universities to include in the project the communities’ college-aged students who will be the future managers and employees of the economic innovations; and (5) to prepare for Stage 2 launch and implementation, with the goal of employing coastal residents in the design, planning and production of beneficial products and services.This project is in response to Track B - CIVIC Innovation Challenge - Resilience to Natural Disasters a collaboration with NSF and the Department of Homeland Security.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1424, "count": 14236 } } }