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=1419&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": "681", "attributes": { "award_id": "2105612", "title": "EAGER: Exploring the Quantum-Mechanical Basis of Odorant Detection by Olfactory Receptors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1559, "first_name": "Engin", "last_name": "Serpersu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-06-01", "end_date": "2023-05-31", "award_amount": 300000, "principal_investigator": { "id": 1561, "first_name": "Piotr E", "last_name": "Marszalek", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1560, "first_name": "Weitao", "last_name": "Yang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The sense of smell is a significant physiological advantage exploited by many organisms including humans and it may be adversely affected by illnesses, such as caused by viral infections as exemplified recently by the COVID-19 disease. Yet, the mechanism by which organisms are able to detect and differentiate between thousands of different odors, which are transmitted by small chemical molecules named odorants, is currently not known. There are two competing hypotheses regarding the first steps in the complex odorant detection reaction, which is carried out by dedicated receptors in specialized olfactory cells. The first hypothesis assumes that odors are encoded in the shape of odorant molecules and their shapes are identified by receptors’ interior cavities into which odorants fit similar to how a correct key fits into a lock. The second hypothesis considers that smell is related to odorant molecules vibrating at specific frequencies and these molecular vibrations are probed and detected by receptors through a complex, quantum mechanics-based electron tunneling mechanism. This project aims at exploring the vibrational hypothesis of odor detection by exploiting cutting edge quantum-mechanics based experimental measurements and computational modeling of the interaction between vibrating odorants and tunneling electrons in the absence and presence of olfactory receptors. Quantum mechanics-based modeling of the interaction between vibrating odorants and electrons will accompany experiments and will clarify the experimental results. This exploratory research will significantly contribute to an understanding of one of the most fundamental biological sensing mechanisms and may help in future developments of artificial “noses” with near single-molecule detection sensitivity. The project will provide ample opportunities for training of postdoctoral, PhD and undergraduate students in multidisciplinary fields involving quantum chemistry, nanotechnology and bioengineering.This research will contribute to the understanding of one of the frontiers of quantum effects in biology with a set of experimental and theoretical investigations aimed at providing evidence confirming or rejecting the model of Quantum Mechanical-based olfaction mechanism. This model, known as the “Vibrational Theory of Olfaction, VTO” relates molecules’ scent to their vibrational spectra and postulates that odor recognition involves quantum mechanical inelastic tunneling of electrons through the odorant-bound receptor. However, this mechanism has remained unproven and controversial. This project will use scanning tunneling microscopy (STM) to measure the tunneling current in the absence and presence of odorant molecules in the nano-junction as well as the dependence of the tunneling current on the bias voltage (tunneling spectroscopy). In the second phase of the project, Odorants will be reconstituted in lipid nanodiscs that will be attached to a conductive surface for STM measurements aimed at capturing inelastic electron tunneling. In addition, computational studies of the inelastic tunneling in the presence of odorant molecules in the tunneling junctions will be used to model the experimental conditions and to provide microscopic understanding of electron tunneling. The inelastic effects calculated will be used to compare with experimental data and provide insight on the roles the vibrational modes. Inelastic electron tunneling through odorant molecules will be studied with state-of-the-art quantum mechanical formalism. This project is supported by the Molecular Biophysics cluster of the Molecular and Cellular Biosciences Division in the Directorate for Biological SciencesThis 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": "680", "attributes": { "award_id": "2030197", "title": "SBIR Phase I: COVID-19: Self-Disinfecting Nanofiber Filters and Reusable Facemasks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1557, "first_name": "Muralidharan", "last_name": "Nair", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2021-09-30", "award_amount": 256000, "principal_investigator": { "id": 1558, "first_name": "Amita", "last_name": "Nakarmi", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 361, "ror": "", "name": "BAONANO, LLC", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 361, "ror": "", "name": "BAONANO, LLC", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a demonstration of a high-quality, self-disinfecting facemask, which is safe, comfortable, and reusable, and will have an immediate impact during the current COVID-19 pandemic. The filter media used in modern facemasks are either inefficient in filtrating submicron size viruses or are difficult to breathe through and highly uncomfortable. Viruses can be active on a facemask for up to one week. With the possibility of contamination and without a self-disinfection function, facemasks must be disposed of after single use. The proposed highly efficient and self-disinfecting filtration technology will solve these problems. The reusability will also directly address the facemask supply shortage as well as waste disposal issue. In the long term, this technology may be further applied as window screens for blocking and disinfecting airborne pathogen particles. The self-disinfection function could be adapted for other personnel protective equipment as well as for environment self-disinfection and for food packaging, etc.This Small Business Innovation Research (SBIR) Phase I project will develop an innovative filtration medium for a disruptive facemask technology by effectively capturing submicron particles including viruses via a nanoparticle-functionalized nanofiber mat that has high filtration efficiency and is thin with low air flow resistance. The technology will also deactivate pathogens in-situ via the ambient, light-enabled, photocatalytic disinfection function of nanoparticles and the subsequent synergetic effects that include physical and chemical disruption of virus membranes and their RNA/DNA. The functionalized catalysts absorb ambient light, producing reactive oxygen species to disinfect pathogens, while the plasmonic effects enhance the light absorption. The charged nanoparticles are firmly embedded on the electrospun nanofibers, and the resulted surface irregularity, the improved charge density, and the hydrophilic absorption further boost the filtration and trapping efficiency through mechanical and electrostatic capture of aerosol particles. Taken together, all these effects may lead to a highly efficient, breathable, reusable facemask with in-situ self-disinfection functionality to combat highly infectious viruses and a broad spectrum of pathogens.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": "679", "attributes": { "award_id": "2049553", "title": "Scaling up commons dilemma experiments for research and education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1551, "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": "2025-12-31", "award_amount": 342017, "principal_investigator": { "id": 1556, "first_name": "Marco A", "last_name": "Janssen", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1552, "first_name": "Allen", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1553, "first_name": "Yi-Chun", "last_name": "Hong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1554, "first_name": "Michael", "last_name": "Simeone", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1555, "first_name": "Lance", "last_name": "Gharavi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Many of the challenges facing contemporary society are collective action problems involving shared resources (known as “commons”). Examples include emission reductions to reduce risks of climate change, and mask-wearing to reduce the spread of COVID-19. In collective action, there is often tension or conflict between the goals of the community and the goals of the individual. Wearing masks and hand washing during the COVID-19 crisis reduces the spread of the virus and flattens the curve, although it might be an inconvenience for the individual. This project investigates three fundamental puzzles in this research area. First, what makes communication so effective at stimulating cooperative outcomes? It is known that communication, even if participants have no ability to enforce promises, has a major positive effect. The goal of the project is to understand what aspects of communication content explains successful cooperation. Second, how does a group’s size impact the ability of its members to cooperate? With smaller groups, it is easier to identify who is free riding. However, small groups may not have sufficient person power to monitor resource appropriation. Third, how do groups that address collective action problems cope with uncertainty and surprises. Various events in 2020, such as the pandemic, are testament to the importance of understanding collective action under uncertainty. Researchers have investigated risk and collective action, with known probabilities of events, but there is a gap in the understanding of how groups cope with unknown unknowns. To address these puzzles, there is a need to scale up controlled behavioral experiments. This project builds a robust platform for “commons” research that also serves as an engaging educational game. The project is mentoring many graduate and undergraduate students, with diverse backgrounds, in research design, data collection, analyses, and programming. Project outcomes include educational resources for college level courses, as well as a special K12 version of the game with accompanying educational material for teachers.The transdisciplinary research team expands a web-based experiment, the Port of Mars, where a group of players make decisions to invest in shared infrastructure and perform actions that benefit themselves and have consequences for the group. The platform enables the conducting of large-scale controlled experiments with in-game chat communication and random events that introduce uncertainty and variance into each play-through. A large number of participants (both college students and members of the general population) take part in a tournament, coined “Mars Madness”, where they compete to become the champion of Port of Mars. Six experimental designs test hypotheses on communication, group size and uncertainty. Data collected during the tournaments is used to train machine-learning models that classify communication data into functional categories which help to better understand how communication among players relate to group performance. In the various tournaments group sizes vary between five and fifty players and they make possible to evaluate the impacts of uncertainty on collective action by controlling players’ knowledge of events and adjusting the thresholds that trigger events. Furthermore, the studies show the extent to which participating in the behavioral experiments improve the understanding of collective action problems.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": "678", "attributes": { "award_id": "2107462", "title": "RAPID International Type I: Assessing Adaptive Responses During COVID-19 Research Collaboration: A Study of Collaborative Contexts", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director" ], "program_reference_codes": [], "program_officials": [ { "id": 1549, "first_name": "Maija", "last_name": "Kukla", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-01", "end_date": "2023-03-31", "award_amount": 198138, "principal_investigator": { "id": 1550, "first_name": "Wesley M", "last_name": "Shrum", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 360, "ror": "https://ror.org/05ect4e57", "name": "Louisiana State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 360, "ror": "https://ror.org/05ect4e57", "name": "Louisiana State University", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "Part 1.This project will improve the U.S. national research system and promote scientific progress by identifying the nature and scope of COVID-19 impacts on international collaboration in research and education. The principal objective is to understand the impact of new information and communications technology (ICT) on the process of networking and collaboration under pandemic conditions, as well as adaptations to these conditions. This assessment allows us to investigate the causes and conditions under which collaborative interactions become disrupted as well as recommend best practices from among those identified as successful by participants. The researchers will analyze both qualitative and quantitative interviews in order to (1) identify principal collaborations and collaborators of each scientist, both local and international; (2) examine the pre-pandemic use of travel, face-to-face meetings, and remote collaborative software, as well as the factors associated with their relative importance across countries; (3) enumerate the types of impacts that COVID-19 had on these collaborations; (4) assess the degree to which technology and scientific practice affected preparedness for disruptions (5) develop a typology of adaptive responses; (6) analyze participant perceptions of the relative effectiveness of these responses on the progress and outcomes of collaboration (7) examine the degree to which men and women scientists had different experiences during the pandemic as well as different responses. Project interviews and analysis are to be widely distributed to provide grounded guidance for policy makers and others seeking to understand the impacts of pandemic conditions on the production of knowledge.Part 2.This project will provide systematic comparisons among a population of scientists across four countries. Data on collaborative practices will be collected from individual scientists, while analysis will yield insight into the nature and process of pandemic collaboration and adaptive practice. International collaborations and U.S. scientists will be compared with scientists in Ghana, Kenya, and India. Previous studies of scientific and engineering communities in these areas have been ongoing since 1994, providing a valuable baseline for comparative assessments of disruptions to international collaboration and the assessment of responses. The project will utilize a combination of qualitative and quantitative interviews with 1100 researchers. The study population includes scientists that engage in international collaborations involving the environment, natural resource management, and sustainability. The two primary research sectors are universities and national research institutes. Our population of scientists and educators is drawn from a group of organizations in and around Baton Rouge, Nairobi, Accra, and Trivandrum. The locations represent diversity in levels of scientific and educational development, with the U.S. community arguably at the summit of international science, followed by Kerala, representing a relatively high level of development, Kenya a medium level, with Ghana comparatively lower. The results of this project will contribute to an understanding of the diversity of adaptive responses among international collaborations and provide guidance for policy makers and managers seeking to understand these differences and design effective interventions.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": "677", "attributes": { "award_id": "2111424", "title": "RAPID International Type I: Identifying Gaps, Interventions and Opportunities of International Collaboration During the Current COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director" ], "program_reference_codes": [], "program_officials": [ { "id": 1546, "first_name": "Maija", "last_name": "Kukla", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2022-04-30", "award_amount": 199768, "principal_investigator": { "id": 1548, "first_name": "Manish", "last_name": "Dixit", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 342, "ror": "https://ror.org/01f5ytq51", "name": "Texas A&M University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1547, "first_name": "Sherecce A", "last_name": "Fields", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 342, "ror": "https://ror.org/01f5ytq51", "name": "Texas A&M University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The ongoing COVID-19 pandemic has not just caused catastrophic disruptions at the socio-economic levels but also adversely impacted international collaboration through a complete disruption in travel, face-to-face interactions, lab work, fieldwork, and hiring. Understanding how this pandemic impacted international collaboration is essential to inform program interventions and foster a more connected and resilient global research community. This study will collect time sensitive information from domestic and international researchers to understand major issues that may have disrupted their research activities, changes they explored to address these issues, and their perception on potential opportunities to facilitate international partnerships in such adverse conditions. This data is essential to inform current and future research programs to include international collaboration components that strengthen the resilience, robustness, and sustainability of international collaborations. By facilitating international research, this study will ensure continued access to expertise, research sites, facilities and equipment for researchers to maintain shared scientific progress to boost the U.S. national economy, prosperity, and human health and well-being. Due to facility shut-downs, safety issues and prescribed non-pharmaceutical interventions, hundreds of students, mostly from underrepresented minority groups are deprived of education and training opportunities embedded in international research. By enabling robust and resilient global partnerships, this study will benefit and influence a broader U.S. student population including underrepresented minority students. The collected data will also explain if existing technology infrastructure needs significant upgrades to strengthen international research collaborations.This study will focus on three collaboration components: team dynamics, project logistics and research operations that govern the success of international research. In a one-year rapid response effort, we will conduct a series of virtual structured interviews and online surveys of at least 90 active projects involving global collaboration, identify and evaluate key issues and changes/interventions, and create a summary matrix of gaps mapped to opportunities. The main goal is to particularly explore the aspects of international collaboration that are integral to its robustness, resilience, and sustainability. We will target international collaboration projects funded by the National Science Foundation (NSF) as well as other federal agencies and multi-country annexes of global agencies such as the International Energy Agency (IEA). The results will offer critical data to explain how such conditions may have impacted these components, revealing key gaps and opportunities to provide directions for making current as well as future international research programs more robust, resilient and sustainable. It will also explain the changes made by the project teams and their effectiveness to address these issues created by the pandemic. The data analysis will further explain: (1) whether and to what extent the type of project, geographic location and demographic attributes of project teams moderated the impact on the robustness, resilience, and sustainability aspects of the project; (2) if certain mechanisms inherent in research programs or solicitations helped or worsened these aspects; and (3) whether certain project activities were more conflicting or challenging than the others. The first-hand data and answers to these questions will form a knowledge base for further research and help identify the areas of improvement in international 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": "676", "attributes": { "award_id": "2036684", "title": "SBIR Phase I: Assistive Robots for Personal Care and COVID-19 Protection", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1544, "first_name": "Muralidharan", "last_name": "Nair", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2022-06-30", "award_amount": 255756, "principal_investigator": { "id": 1545, "first_name": "Michael", "last_name": "Dooley", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 359, "ror": "", "name": "LABRADOR SYSTEMS, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 359, "ror": "", "name": "LABRADOR SYSTEMS, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project advances the state-of-the art of an emerging class of vision-based, autonomous navigation technologies to open new possibilities for low-cost/high-performance personal assistive robots. The robotics solution enables mobility-impaired individuals to have more agency over their environment and enjoy a higher quality-of-life. This helps address the severe shortage of caregivers for the elderly and post-acute care patients by empowering individuals to maintain their independence, extending the impact of caregivers, and reducing the cost of care in both home and facility settings. Additionally, by providing affordable and reliable isolation support in COVID-19 care settings, the proposed solution can help decrease the financial burden and increase the public health outcomes associated with COVID-19 disease management. The core robotics solution has an immediate addressable market of 11 million high-needs users in the U.S. alone, with projected revenues of roughly $1.65 Billion five years after product launch. Further commercialization opportunities come from licensing parts of the developed navigation technology for other robotics applications and developing an ecosystem of complementary products around the core robotics solution.This Small Business Innovation Research Phase I project seeks to enable a new generation of assistive service robots that are comparable to commercial robots in performance, but significantly more affordable for individual use and personal care applications. The innovation adopts emerging visual positioning technologies from Augmented Reality to enable robust navigation for mobile robots using low-cost, consumer-grade electronics, while addressing a key limitation of visual positioning systems namely, that external lighting conditions and other changes in an environment can dramatically impact their performance. The innovation addresses these challenges via a combination of hardware and software that learns and stabilizes the highest value visual elements of the environment to maintain persistency across lighting conditions and long periods of time — a development critical to making assistive robots cost-effective for adoption at a large scale. Research objectives include: fully developing and integrating the visual persistency system, to achieve accurate and replicable robot navigation performance across a representative range of lighting conditions and visual characteristics of the target operating environments and benchmarking the resulting solution against state-of-the art technologies, to demonstrate its superior performance (i.e., it can successfully localize in at least 90% of cases where other solutions fail).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": "675", "attributes": { "award_id": "2100385", "title": "RAPID: American Indian Authorities, Trust, and Collective Action during the Covid-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1542, "first_name": "Paul", "last_name": "Huth", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-01", "end_date": "2021-11-30", "award_amount": 187664, "principal_investigator": { "id": 1543, "first_name": "Aila", "last_name": "Matanock", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 176, "ror": "", "name": "University of California-Berkeley", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will determine how citizens react to and utilize information from different sources on public health policies to inform their individual decisions on responding to the COVID-19 pandemic. The project will provide systematic and rigorous evidence on how and why citizens’ perceptions of trust and legitimacy condition their response to different sources of information from authorities on public health policies. The results of this project will contribute to improving the understanding of how public and private authorities can build trust with citizens and communities through the information they provide on public health policies to combat COVID-19 and future public health crises.The research will use multi-wave, survey experiments on rural-based national and select state populations samples to examine which sources of information providing guidelines on public health policies are deemed trustworthy and legitimate by citizens. Those evaluations in turn will be examined to determine how they influence individual choices about compliance with lockdown orders and the use of vaccines when they become available. The findings of the project will advance basic research on the causes of collective action, the public’s reaction to and use of scientific information and expertise, the social determinants of improving public health policies, and the efficacy of different information strategies in times of crisis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "674", "attributes": { "award_id": "2032579", "title": "STTR Phase I: An Artificial Intelligence (AI)-based algorithm using nanosensor-based salivary analytics to predict clinical outcomes in symptomatic COVID-19 patients", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1539, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-15", "end_date": "2022-07-31", "award_amount": 267239, "principal_investigator": { "id": 1541, "first_name": "Huma", "last_name": "Jafry", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 358, "ror": "", "name": "NANOINNOVATIONS, LLC", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1540, "first_name": "Xiaoqian", "last_name": "Jiang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 480, "ror": "https://ror.org/03gds6c39", "name": "The University of Texas Health Science Center at Houston", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 358, "ror": "", "name": "NANOINNOVATIONS, LLC", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to provide U.S. hospitals with a tool to help accurately predict the expected severity of illness for COVID-19 infected patients at the time of initial diagnosis. A simple interface uses Artificial Intelligence-based predictive algorithms to help hospitals make informed and accurate decisions about which patients require specific care and treatment interventions. This enhanced process allows hospitals better and faster decision-making on patient care and treatments, redirecting hospital resources (including staff, hospital beds, ICU) for maximum effectiveness. In the longer term, the platform can be adapted to predict illnesses related to other infectious diseases, and also scaled for countries where availability of hospital infrastructure is limited.This Small Business Technology (STTR) Phase I project will develop a completely new category of medical diagnostic and prognostic tools via a novel approach that relies on analysis of a complex multi-variate signal, reflective of the patient’s entire salivary metabolome and proteome. Artificial intelligence tools will be used to see if signal clusters correlating with patient outcomes can be identified. This is a radical departure from traditional medical diagnostics which evaluate individual biomarkers for a clinical diagnosis. Such approaches are ill suited to the task of predicting future patient outcomes. The scope of this pilot phase work is the development of an effective algorithm and understanding algorithm efficacy and reliability in prediction and classification of outcomes for COVID-19 patients. The goals of the pilot project are to: (i) obtain COVID-19 patient bio-fluid samples, and (ii) develop machine learning techniques for an effective predictive algorithm. Multiple machine learning techniques and comparison strategies will be used for algorithm development and efficacy testing.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": "673", "attributes": { "award_id": "2049300", "title": "Collaborative research: The Intergenerational Effects of the COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1536, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2022-12-31", "award_amount": 174032, "principal_investigator": { "id": 1538, "first_name": "Jenna", "last_name": "Nobles", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1537, "first_name": "Felix", "last_name": "Elwert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "This project examines the effects of COVID-19 exposure during pregnancy on birth outcomes, over time, and across different groups defined by different sources of disadvantage. The COVID-19 pandemic is a large shock likely to affect infant health through multiple pathways including maternal infection, stress and anxiety, economic hardship, and access to prenatal care. Because these factors differ across groups in the United States, the impact of COVID-19 on birth outcomes will likely be stronger among groups with fewer advantages and certain demographic groups, exacerbating differences in the United States. These effects are critical to understand because birth outcomes predict health and socioeconomic attainment throughout the life course. This study relies on causal inference techniques exploiting variation in infection rates across time and place to capture the consequences of the pandemic on differences in birth outcomes, in particular intrauterine growth restriction, a key predictor of early-life cognition, education, and ultimately earnings. Birth records obtained at the state level with early release are used to provide the earliest possible evidence. Research focuses on six states that provide large and diverse samples across areas where the pandemic has unfolded with significant variation. Time-varying data on COVID incidence and mortality, and local official responses are linked to these data.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": "672", "attributes": { "award_id": "2049529", "title": "Collaborative research: The Intergenerational Effects of the COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1534, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2022-12-31", "award_amount": 155773, "principal_investigator": { "id": 1535, "first_name": "Florencia", "last_name": "Torche", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project examines the effects of COVID-19 exposure during pregnancy on birth outcomes, over time, and across different groups defined by different sources of disadvantage. The COVID-19 pandemic is a large shock likely to affect infant health through multiple pathways including maternal infection, stress and anxiety, economic hardship, and access to prenatal care. Because these factors differ across groups in the United States, the impact of COVID-19 on birth outcomes will likely be stronger among groups with fewer advantages and certain demographic groups, exacerbating differences in the United States. These effects are critical to understand because birth outcomes predict health and socioeconomic attainment throughout the life course. This study relies on causal inference techniques exploiting variation in infection rates across time and place to capture the consequences of the pandemic on differences in birth outcomes, in particular intrauterine growth restriction, a key predictor of early-life cognition, education, and ultimately earnings. Birth records obtained at the state level with early release are used to provide the earliest possible evidence. Research focuses on six states that provide large and diverse samples across areas where the pandemic has unfolded with significant variation. Time-varying data on COVID incidence and mortality, and local official responses are linked to these data.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": 1419, "count": 14184 } } }