Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-approved
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-approved", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-approved", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=-approved", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-approved" }, "data": [ { "type": "Grant", "id": "12470", "attributes": { "award_id": "2333103", "title": "Research Infrastructure: RCN: A Comprehensive Toolkit for Interdisciplinary Team Science, & its Context-Dependent Application", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Cross-BIO Activities" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28409, "first_name": "Ruth", "last_name": "Varner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 308, "ror": "", "name": "Ohio State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "Interdisciplinary Team Science is needed to address numerous challenges facing humanity, from pandemics, to the transformation of the energy sector, to the largest challenge our species has ever faced, that of climate change. Since 2020, the National Science Foundation has invested in a collection of large-team Biology Integration Institutes to advance innovation in priority areas across the biological sciences, with 13 currently funded Institutes. This collection of teams presents a powerful opportunity for advancement, in addition to that from their science: the collection and organization of the Interdisciplinary Team Science tools they have developed or applied, and an evaluation for the context-dependency of their application - i.e. how host organization (e.g. university size, Carnegie Classification System, populations served) and team characteristics (e.g. size, governance structures, career stage, demography) influence teams’ prioritized needs for different kinds of tools. In this Research Coordination Network, the Biology Integration Institutes will work together to pool resources, evaluate the context-dependency of use, and disseminate the collected Toolkit for Interdisciplinary Team Science. This will promote scientific progress across a range of areas where large-team integration is essential. Technical Description: This Research Coordination Network (RCN) unites the Biology Integration Institute (BII) program members to share knowledge, tools and perspectives around interdisciplinary teaming, in a single organized Toolkit, spanning elements from Governance Plans to Social Media Best Practices. It also evaluates how the value to teams of these tools varies based on organizational and team context, across a range of axes (e.g. type of university). Lastly, it disseminates the Toolkit and results of the context-dependency analysis, through a broad network of scientific teams and communities. The Merit of the proposed RCN project is in leveraging the opportunity of the NSF BII Program to harvest the Interdisciplinary Team resources that hundreds of scientists across these organizations have developed or assembled, and ‘field tested’. By then evaluating the relative usefulness and importance of tools across teams, and expanding the evaluation to other large teams that the RCN’s Team Scientists have worked with, the RCN will provide qualitative insights into context-dependency of tool usage, which is a key knowledge gap among team science practitioners. The impacts of the proposed work are to advance teaming across a range of communities and democratize the knowledge of teaming resources, which can present a significant impediment to broadened participation in proposing and leading large science teams. A range of early-career researchers will also be directly involved in implementation of this RCN, and associated training.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": "12471", "attributes": { "award_id": "2315533", "title": "Collaborative Research: The Economics of Port Infrastructure", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Economics" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28410, "first_name": "Myrto", "last_name": "Kalouptsidi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 455, "ror": "https://ror.org/03vek6s52", "name": "Harvard University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award funds a project in the economics of transportation infrastructure that focuses on the role of shipping ports in international trade. Such infrastructure is crucial for the smooth functioning of international trade. Because more than 80% of traded goods (amounting to 11 billion tons and about $20 trillion) are carried by ships, shipping ports are the literal gateway to international trade. The advent of global sourcing, whereby firms source their inputs from further away suppliers, as well as just-in-production, whereby firms expect their inputs to arrive at the moment they need them, has made ports even more pivotal in recent decades. This became painfully apparent during the “Great Supply Chain Disruptions” induced by the Covid-19 pandemic, which upended supply chains, and generated enormous costs to importers and exporters worldwide, who faced massive delays in obtaining their goods. In this uncertain environment, what are the returns to investing in transportation infrastructure? And how should funding be coordinated and spent? What are the drivers of port performance and why are ports so prone to disruption? This project seeks to provide an answer to these questions, by combining a collection of novel data sets on ports, with a state-of-the-art modeling framework for port technology and demand for port services. The results of this project will be useful for making policy decisions about infrastructure investments, including both where ports should be located, what kinds of investment has the highest returns, and whether or not these decisions should be decentralized. The researchers construct a unique database on global ports that relies on data collected by private providers, as well as their own data collecting efforts. The data includes satellite vessel position data, detailed port charges, as well as union membership. They create a dataset on port infrastructure by manually collecting historical satellite images of world ports from Google Earth. In order to evaluate the role of transportation infrastructure one needs the following ingredients: infrastructure technology to understand both the dynamics of congestion, as well as how investment in infrastructure affects congestion; and a demand system for transportation services. This is necessary both because demand endogenously responds to infrastructure improvements, but also in order to quantify their welfare gains. Using insights from queueing theory, the researchers construct and estimate a micro-model of port technology that takes port inputs, such as infrastructure, labor, cranes, and productivity, and produces output, namely time at port. That setup endogenously generates convex costs of congestion. The researchers then estimate a demand system for port services: potential exporters/importers decide which port to use and obtain utility from lower time at port, shorter distance to their origin and destination, lower port prices, as well as other characteristics of the port. The researchers combine their estimates of technology with their estimates for port demand to estimate the returns to port infrastructure investment. The research advances knowledge by shedding light on how ports operate and how they contributed to the Great Supply Chain Disruption. It takes a deep look into port technology and shows that it renders ports inherently disruptive. This also illustrates the role of uncertainty and the impact of different inputs on resilience.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": "12472", "attributes": { "award_id": "2321398", "title": "MCA: Understanding cellulose synthase complex in planta using single molecule methods", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Cellular Dynamics and Function" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28411, "first_name": "Shi-You", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 521, "ror": "https://ror.org/05hs6h993", "name": "Michigan State University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "Cellulose is predominantly produced by plants as the main load-bearing component of the plant cell wall to provide strength and play critical roles in plant cell growth and development. As “the most abundant biopolymer on earth,” cellulose also provides a great potential resource for biofuels and renewable biomaterials towards a carbon-negative economy. Despite its importance, the detailed molecular mechanism underlying cellulose biosynthesis in plants remains largely elusive. This project addresses this knowledge gap by applying advanced microscopic imaging methods to capture the dynamic activity of cellulose synthase in living plants, achieved through a synergistic collaboration between a plant biologist and an optical physicist. The research team will exploit and develop new microscopy techniques to allow real-time visualization of cell wall biosynthesis in growing plants, specifically the enzymes that are responsible for cellulose synthesis at the subcellular and the molecular levels. The results will provide new insights into cellulose biosynthesis and critical information required for further rational design and production of bio-based materials. The project also aims to foster collaboration and multidisciplinary team training of young researchers and students in the current education and outreach programs at Michigan State University and South Dakota School of Mines and Technology to explore their interests in plant science and discover their passion in science and engineering.The biosynthesis of cellulose has been described as a spatially and temporally controlled process carried out in the plasma membrane (PM) by a cellulose synthase complex (CSC) containing multiple cellulose synthases (CESAs). In the Arabidopsis genome, ten putative cesa genes are identified and biochemical and genetic studies have revealed that at least three different CESA isoforms at a 1:1:1 ratio are required for cellulose synthesis in planta. However, the architecture of CSC and its dynamic function in synthesizing cellulose has been largely elusive. This project aims to exploit and develop microscopy approaches to correlatively image the assembly and dynamics of CSCs in planta and cellulose microfibril structure in situ. The putative CESA domains that may play critical roles in CSC assembly and trafficking during cellulose biosynthesis have been engineered to express fluorescence protein tags in corresponding cesa knockout backgrounds for in planta imaging using super resolution microscopy. Specifically, the lattice light-sheet (LLSM) and oblique selective plane illumination microscopy systems with enhanced photon efficiency are used for 3D/4D single molecule tracking to improve localization accuracy and deep tissue imaging, and fluorescence resonance energy transfer (FRET) and time correlated single photon counting methods are used to measure CESA-CESA interactions. Furthermore, the 3D/4D trajectory data are analyzed to correlate with other preliminary imaging results, such as ultrastructure of cellulose microfibrils imaged by AFM and physicochemical properties of cell walls imaged by stimulated Raman scattering microscopy. The findings from this project will allow us to test our working hypotheses and formulate future research directions.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": "12473", "attributes": { "award_id": "2327836", "title": "IHBEM: Data-driven integration of behavior change interventions into epidemiological models using equation learning", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 2203, "first_name": "Julie", "last_name": "Swann", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 245, "ror": "https://ror.org/04tj63d06", "name": "North Carolina State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Given the antigenic characteristics of a virus, human behavior is the single most important determinant of disease transmission. Human behaviors relevant to disease spread such as social distancing, wearing face coverings, or testing when asymptomatic depend on a host of factors including risk perceptions, physical ability as well as the availability of resources and opportunities. Policy interventions by health agencies or other decision makers can impact these factors to alter human behaviors. Using decision models to tailor these interventions by time and sub-population can ensure efficiency (e.g., low cost), effectiveness (e.g., less hospitalizations), and equity (e.g., fairness in access to pharmaceuticals). The overall goal of this project is to incorporate behavior change driven by public health interventions into mathematical epidemiological models to inform decision making and policy evaluation during infectious disease outbreaks. The investigators consider respiratory diseases in general, and use COVID-19 as an example to validate the approach and quantify impact. The proposed methods can be generalized to other applications where policy makers target behavior change, such as medication adherence.In Aim 1, the investigators will trace the impact of policy interventions on infection-preventive behaviors through mechanisms of action (i.e., capability, opportunity, and motivation). Nine types of policy interventions will be considered (education, persuasion, incentives, coercion, restriction, training, nudging, modeling, and enablement) in relation to two types of preventive behavior – interpersonal protection (i.e., social distancing, wearing a face mask) and service utilization (i.e., testing, vaccination). The empirical work involves a dynamic meta-analysis of interventions to reduce the spread of COVID-19, supplemented by Delphi methods. The investigators will develop an online tool that will enable researchers to contribute to the meta-analysis and use the resultant weighted-average effect sizes as inputs for agent-based modeling. The results of Aim 1 will be operationalized by integrating adaptive human behaviors into an agent-based model (ABM). However, realistic ABMs with a large number of agent types and complex behavioral and social processes are computationally intensive to simulate, analytically intractable, and may not be generalizable. These drawbacks may inhibit the comprehensive analysis and validation of ABMs and thereby prevent their utilization for decision- and policy-making during a pandemic. Thus, in Aim 2, the investigators propose an equation learning framework to derive ordinary differential equation (ODE) models from ABMs. The investigators also introduce novel regularization techniques that incorporate biophysical constraints to provide interpretable results for decision-makers. These ODE models and the learned functional forms approximating the impact of interventions on behavioral and social processes that drive disease spread will be used in Aim 3 to inform policies through bilevel optimization models.This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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": "12474", "attributes": { "award_id": "2320685", "title": "Equipment: MRI: Track 1 Acquisition of an Illumina NextSeq 2000 Sequencing Instrument", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "Major Research Instrumentation" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28412, "first_name": "Catherine", "last_name": "Klapperich", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 168, "ror": "", "name": "Trustees of Boston University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award supports the acquisition of an Illumina NexSeq 2000 next generation sequencing system to be housed at the Design, Automation, Manufacturing, and Process (DAMP) Laboratory at Boston University. The DAMP Lab is an automated biology laboratory comprised of eight liquid handling robots that can run thousands of experiments at a time in tiny wells of liquid. These robots do a much better and faster job of transferring liquids from place to place than humans can. The DAMP Lab performed thousands of COVID tests during the pandemic, and now its instruments are used to create and study new molecular tools. The Illumina sequencer will help researchers study the genomic sequences of these new molecular tools. Small changes in the sequence can lead to big changes in performance. Next generation sequencing has an unparalleled ability to see which changes enhance or diminish function. This new instrument will help support the design of better drugs and new diagnostics and biosensors. The DAMP Lab at Boston University is a Core Facility that serves BU, Boston area academics and industry, and has the potential to serve researchers across the nation through our completely cloud based automated laboratory system. Access to next generation sequencing technology will unlock new insights across the convergent areas of biotechnology and bioengineering. Addition of the Illumina NexSeq 2000 will impact three major areas of research at BU: Organoid Profiling, Environmental and Water Monitoring, and Biosensing and Diagnostics. Over the pandemic, Boston University built an automated molecular biology laboratory, now part of the DAMP Lab, that is focused on accelerating synthetic biology research. Addition of this instrument to the existing suite of automation will dramatically increase the services we can offer users. This unique cloud-based laboratory has already made many sophisticated wet biological protocols accessible to laboratories and researchers who don’t have the in-house resources or expertise to perform them – or who might not have a wet laboratory at all. Addition of this instrument will open next generation sequencing (NGS) to these same groups.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": "12475", "attributes": { "award_id": "2241853", "title": "Predicting fate and transport of antibiotic resistance genes in streams", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EnvE-Environmental Engineering" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28413, "first_name": "Kaoru", "last_name": "Ikuma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 192, "ror": "https://ror.org/04rswrd78", "name": "Iowa State University", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true }, "abstract": "Antimicrobial resistance has become a global public health threat. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that more than 2.8 million antimicrobial-resistant infections occur each year. Antimicrobial resistance (AMR) occurs when pathogenic microorganisms no longer respond to drugs such as antibiotics making it very difficult to treat infections and control contagion and disease spread. AMR occurs naturally and develops over time as microorganisms exchange genetic materials (e.g., antibiotic resistance genes) in environmental media including air, water, soils, and sediments. There is growing concern about the roles of surface water systems and wastewater treatment plants as reservoirs and sources for antibiotic resistance genes (ARGs). However, the availability of validated models that can accurately predict the fate and transport of ARGs in surface water systems has remained elusive. The overarching goal of this project is to develop and experimentally validate a computational model to predict the fate and transport of ARGs in surface water systems using a river in Central Iowa as a model system. To advance this goal, the Principal Investigators (PIs) propose to combine and integrate laboratory experiments, field measurements, and physics-based computational modeling to accurately predict the fate and transport of ARGs in surface water systems including streams and rivers. The successful completion of this project will benefit society through the development of a new and validated model to improve the ability to predict and assess the risk of ARG spread and transmission in surface water systems. Additional benefits to society will be achieved through student education and training including the mentoring of two graduate students and one undergraduate student at Iowa State University.Predicting the fate and transport of antibiotic resistance genes (ARGs) in rivers is complicated by the large number of processes involved. As with all models of water quality in rivers, a model for ARG transport must account for advection and dispersion by the river’s flow, as well as lateral inflows into the river including stormwater/agricultural runoffs and the discharges of wastewater treatment plant effluents. In addition, ARGs can exhibit different form factors in surface water systems including intracellular DNA (iDNA), free extracellular DNA (eDNA), and particle-associated DNA. Potentially important processes that control the fate and transport of ARGs in rivers include sorption to particles, gene transfer to live cells, gene replication, and mobilization. Because replication, horizontal gene transfer, and decay can occur in river sediments, an accurate model of ARG fate in rivers needs to account for the transport between the water column and the sediment bed of a river. Building the results of preliminary modeling investigations of the fate and transport of ARGs in a model river system, the Principal Investigators (PIs) of this project propose to test the hypothesis that the inclusion of processes that account for sediment bed and eDNA exchanges will improve the predictive capability of their model. The specific objectives of the research are to (1) evaluate the predictive capability of the PIs’ revised ARG transport model using field measurements in a river and identify parameters requiring further study; (2) measure these parameters in batch and mesocosm experiments; and (3) test the robustness of the model with additional field measurements and analyses. Objective 1 will focus on field measurements downstream of a wastewater treatment plant (WWTP) in Ames (Iowa) that are designed to evaluate the sediment and eDNA contributions to the model predictive capability. Objective 2 will consist of laboratory experiments targeting the parameters identified in Objective 1 with the goal of determining realistic ranges of values for the model parameters. In Objective 3, the estimated parameters from Objective 2 will be used as inputs to the model to predict concentrations of ARGs downstream of the WWTP in Objective 1 followed by field measurements designed to evaluate the model predictive capability and the robustness of its parametrization. To implement the education and training goals of this project, the PIs plan to integrate the research findings into a project-based course entitled “Introduction to Research” which is a required course for the newly launched B.S. degree program in environmental engineering at Iowa State University.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": "12476", "attributes": { "award_id": "2327814", "title": "Collaborative Research: IHBEM: Three-way coupling of water, behavior, and disease in the dynamics of mosquito-borne disease systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28414, "first_name": "Oscar", "last_name": "Santos Vega", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Complex behavioral responses to information from public health officials, social media, and elsewhere during the COVID-19 pandemic laid bare the limitations of the simplistic assumptions that epidemiological models have traditionally made about human behavior. The investigators of this project hypothesize that human behavior may also play a key role in why diseases transmitted by Aedes mosquitoes, such as dengue and Zika, have been so difficult to control. Aedes mosquitoes lay eggs in household water storage containers, meaning that behaviors related to water storage, water consumption, and water container management impact mosquito populations and, thereby, diseases transmitted by these mosquitoes. The central objective of this project is to understand how humans make decisions about preventive actions against Aedes-borne diseases and how those actions in turn affect disease dynamics and subsequent individual-level decision-making. The project will focus on the city of Ibagué, Colombia, where public health officials have long used behavioral approaches to intervene against Aedes-borne diseases. Empirical social science research will investigate how individuals respond to these interventions and characterize differences among individuals in their responses. Mathematical modeling research will estimate the effectiveness of these interventions at the population level. Throughout the project, a close connection with community members and local public health officials will be cultivated to ensure the effective translation of project outcomes. Training and capacity building activities will extend the impacts of the project to settings beyond Ibagué.This project aims to develop a mechanistic understanding of the role of behavior in infectious disease dynamics and mathematical modeling tools that are capable of accounting for those mechanisms, with the ultimate goal of enabling more effective use of public health interventions. The project will be grounded in empirical social science research in Ibagué, a city in Colombia with one of the highest urbanization rates and Aedes-borne disease transmission rates in the country. A combination of observational and experimental approaches will be used to characterize heterogeneity in the adoption of mosquito prevention behaviors in and around the home and to understand the cues that drive the adoption, or neglect, of those behaviors. These empirical findings will be used to develop a mathematical model of individual decision-making around the use of mosquito prevention behaviors in response to individual-level behavioral dispositions that change over time as cues arise and subside. This decision-making model will then be incorporated into an agent-based model of Aedes-borne disease transmission that will be used to infer the effectiveness of behavioral interventions that public health officials use to control Aedes-borne diseases in Ibagué. Finally, a suite of simpler macroscopic models will be developed and assessed with respect to their ability to capture effects of behavioral interventions on epidemiological dynamics simulated with the agent-based model. The ultimate outcome of the project will be the development and validation of minimally complex mathematical models that are capable of predicting responses of epidemiological dynamics to behavioral interventions.This project is jointly funded by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS), the Established Program to Stimulate Competitive Research (EPSCoR), and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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": "12477", "attributes": { "award_id": "2334144", "title": "Planning: CRISES: Building a Center for Resiliency in Rapidly Developing Communities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "CRISES-R&I in Sci, Env&Society" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28415, "first_name": "Samantha", "last_name": "Mosier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 709, "ror": "https://ror.org/01vx35703", "name": "East Carolina University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The United States is experiencing new rapid population shifts. High housing prices, the lingering impacts from the COVID-19 pandemic, and the pending retirement of many individuals within the Baby Boomer generation are underlying factors to population migratory patterns. The impact of such population shifts is of concern given that many of the areas that are and have experienced rapid growth are those with historically lower costs of living, are identified as or near Justice40 disadvantaged areas, and are perceived to have more desirable climates. Communities experiencing rapid population growth must reconcile the numerous challenges of development associated with growth and establish appropriate and timely environmental, economic, and social policy responses. The proposed planning activities will explore, identify, and establish interdisciplinary mechanisms for understanding resiliency in rapidly developing complex systems. Planning participants for the proposed Center for Resiliency in Rapidly Developing Communities (CCRDC) come from a diverse range methodological perspectives and disciplines. Population growth is a contemporary and system level concern for appropriately and adequately addressing climate change. Rapid population growth, whereby a region or community experiences a drastic increase in positive net migration, is particularly concerning given the potential to create or exacerbate climate change challenges. While the wicked problems concept and the work of Limits to Growth (e.g., socio-ecological systems or socio engineered-ecological systems) guides the initial planning efforts, the planning activities establish a basis for structuring sound basic-science research protocols and plans that can bridge across various theories and potentially lead to novel contributions to existing theoretical bases or lead to the creation of new theoretical constructs for understanding resiliency responses to rapid population growth's effects on impacted and surrounding communities. The planning activities for the CCRDC platform provide an opportunity for participant researchers to help define the scope and approach of the Center’s future research and outreach activities. This helps to strengthen the ability to achieve the long-term goal for the CCRDC, which is to provide timely and appropriate resiliency-based recommendations and support to communities undergoing current or anticipated future growth. The project will engage an interdisciplinary set of researchers with the intent to later engage in transdisciplinary and co-produced research.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": "12478", "attributes": { "award_id": "2315629", "title": "Constraints, Rigidity, and Risk in Global Supply Chains", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Economics" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28416, "first_name": "Diego", "last_name": "Comin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Recent major shocks (e.g., US-China tariff increases and the COVID-19 pandemic) have reverberated through global supply chains. As a result, there is growing concern that dependence on foreign inputs exposes firms, and the macro-economy more broadly, to heightened risks. For example, supply chain disruptions have been widely blamed for the resurgence of inflation. Moreover, recent policy reforms have encouraged reshoring of manufacturing production. This project examines how key microeconomic features of input sourcing, including capacity constraints, sunk investments, and decision making under uncertainty, give rise to macroeconomic risks. The findings will enable policymakers to evaluate supply chain risks more accurately and design policies to manage those risks.In the first part of the project, the researchers will study how occasionally binding capacity constraints in the supply chain, and shocks to them, shape inflation. When domestic or foreign firms exhaust their capacity, they have an incentive to raise their price, by increasing the markup over marginal cost. In the aggregate, binding constraints shift domestic and import price Phillips Curves, like cost-push shocks in reduced form, and thus fuel inflation. Building this idea into a modern multisector, open economy, New Keynesian model, the project applies the framework to interpret recent US data and study optimal monetary policy responses to supply chain disruptions. In the second part of the project, the researchers investigate how firms invest in their supply chain capacity, and how those investments matter for the propagation of macroeconomic shocks. The project develops a framework in which firms undertake irreversible (sunk) investments to source inputs, which lock buyers into particular suppliers in the short run, who have limited supply capacity and are subject to shocks. It then explores how risk averse buyers invest in a portfolio of suppliers to manage risk. Further, it examines how those investments affect the propagation of shocks, and whether trade and industrial policies may correct inefficiencies in risk taking to mitigate aggregate volatility. The novelty of these projects lies in their synthesis of micro- and macro-perspectives on supply chain risks, as needed for policy analysis.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": "12479", "attributes": { "award_id": "2315535", "title": "Collaborative Research: The Economics of Port Infrastructure", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Economics" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28417, "first_name": "Theodore", "last_name": "Papageorgiou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 425, "ror": "https://ror.org/02n2fzt79", "name": "Boston College", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award funds a project in the economics of transportation infrastructure that focuses on the role of shipping ports in international trade. Such infrastructure is crucial for the smooth functioning of international trade. Because more than 80% of traded goods (amounting to 11 billion tons and about $20 trillion) are carried by ships, shipping ports are the literal gateway to international trade. The advent of global sourcing, whereby firms source their inputs from further away suppliers, as well as just-in-production, whereby firms expect their inputs to arrive at the moment they need them, has made ports even more pivotal in recent decades. This became painfully apparent during the “Great Supply Chain Disruptions” induced by the Covid-19 pandemic, which upended supply chains, and generated enormous costs to importers and exporters worldwide, who faced massive delays in obtaining their goods. In this uncertain environment, what are the returns to investing in transportation infrastructure? And how should funding be coordinated and spent? What are the drivers of port performance and why are ports so prone to disruption? This project seeks to provide an answer to these questions, by combining a collection of novel data sets on ports, with a state-of-the-art modeling framework for port technology and demand for port services. The results of this project will be useful for making policy decisions about infrastructure investments, including both where ports should be located, what kinds of investment has the highest returns, and whether or not these decisions should be decentralized. The researchers construct a unique database on global ports that relies on data collected by private providers, as well as their own data collecting efforts. The data includes satellite vessel position data, detailed port charges, as well as union membership. They create a dataset on port infrastructure by manually collecting historical satellite images of world ports from Google Earth. In order to evaluate the role of transportation infrastructure one needs the following ingredients: infrastructure technology to understand both the dynamics of congestion, as well as how investment in infrastructure affects congestion; and a demand system for transportation services. This is necessary both because demand endogenously responds to infrastructure improvements, but also in order to quantify their welfare gains. Using insights from queueing theory, the researchers construct and estimate a micro-model of port technology that takes port inputs, such as infrastructure, labor, cranes, and productivity, and produces output, namely time at port. That setup endogenously generates convex costs of congestion. The researchers then estimate a demand system for port services: potential exporters/importers decide which port to use and obtain utility from lower time at port, shorter distance to their origin and destination, lower port prices, as well as other characteristics of the port. The researchers combine their estimates of technology with their estimates for port demand to estimate the returns to port infrastructure investment. The research advances knowledge by shedding light on how ports operate and how they contributed to the Great Supply Chain Disruption. It takes a deep look into port technology and shows that it renders ports inherently disruptive. This also illustrates the role of uncertainty and the impact of different inputs on resilience.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": 1385, "pages": 1405, "count": 14046 } } }