Grant List
Represents Grant table in the DB
GET /v1/grants?sort=funder
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=funder", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=funder", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=funder", "prev": null }, "data": [ { "type": "Grant", "id": "9536", "attributes": { "award_id": "2030139", "title": "Compounding Crises: Facing Hurricane Season in the Era of COVID-19", "funder": null, "funder_divisions": [], "program_reference_codes": [ "CK090", "RND123" ], "program_officials": [], "start_date": null, "end_date": null, "award_amount": 199890, "principal_investigator": null, "other_investigators": [], "awardee_organization": null, "abstract": "Test", "keywords": [ "covid", "research" ], "approved": true } }, { "type": "Grant", "id": "2500", "attributes": { "award_id": "2017789", "title": "Equity and Sustainability: A framework for Equitable Energy Transition Analyses", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EnvS-Environmtl Sustainability" ], "program_reference_codes": [], "program_officials": [ { "id": 7114, "first_name": "Bruce", "last_name": "Hamilton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2023-06-30", "award_amount": 399915, "principal_investigator": { "id": 7116, "first_name": "Destenie", "last_name": "Nock", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Decisions regarding transitions from traditional energy sources such as fossil fuels to more sustainable, renewable energy systems impact multiple constituencies, including the most vulnerable members of society. This research addresses two questions: (1) What are transition pathways from non-renewable energy sources (such as fossil fuels) to renewable energy sources (such as wind and solar) for the US electricity sector that can best balance the (sometimes conflicting) objectives of the transition, while accounting for social equity and sustainability? (2) How can transition to a low-carbon electricity system be done in a way that minimizes adverse impacts on the most vulnerable members of society? This research targets creating a new way to account for social equity in the sustainability analysis of transitions to new energy systems, which may help guide decision-makers. \n\nThere are many decision makers and constituencies in energy system planning, each of which may make decisions or influence decisions according to their own versions of the desired goals. This research builds and expands upon previous research in three key ways that permit a more robust sustainability assessment of future electricity systems, and incorporates social equity into the energy transition discussion. First, an electricity system expansion model is coupled with a system sustainability model and then examined to ask how increasing carbon constraints are likely to impact power system development, and how important regional cooperation is likely to be in achieving a fully decarbonized US electricity system. Second, social equity will be an integral part of the sustainability analysis framework, thus displaying how other facets of sustainability impede or support an equitable energy transition. Third, to illuminate the social equity trade-offs, how regional cooperation may impact job and price equity around the country will be investigated. This research will be a system sustainability analysis for the entire US that incorporates multiple metrics for social equity, while capturing impacts of integrating intermittent renewables in the grid. The PI will develop an open-source data analysis tool for electricity sustainability analysis, enriching the discussion and uncovering the interactions among sustainability criterion at a national scale. The social equity focused framework is targeted to facilitate national discussions about how energy transition will impact communities in the US. This framework may also help support planning for job recovery of those most affected by the retirement of fossil fuel generation.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10189", "attributes": { "award_id": "2127909", "title": "Collaborative Research: The Role of Elites, Organizations, and Movements in Reshaping Politics and Policymaking", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Build and Broaden" ], "program_reference_codes": [], "program_officials": [ { "id": 1532, "first_name": "Lee", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2025-05-31", "award_amount": 233731, "principal_investigator": { "id": 26135, "first_name": "Periloux", "last_name": "Peay", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26134, "first_name": "Jennifer L", "last_name": "McCoy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 300, "ror": "", "name": "Georgia State University Research Foundation, Inc.", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Arguably, the current political climate is the function of three seemingly distinct, yet interrelated, ongoing phenomena: (1) a contentious, problem-laden political environment, (2) grassroots organizations driving unprecedented levels of engagement and turnout, and (3) national movements driving discourse, preferences, and reform around long-held policy grievances. The combination of contentious politics and an energized electorate can result in record turnout despite a raging pandemic. The PIs examine how these features of the American polity shape public and institutional political behaviors. The project aims to build a network, and supportive infrastructure, to better understand how political elites, organizations, and movements in key political locations work to drive participation, preferences, and policymaking. \n\nThe project examines two broad research questions. The first question is: How do organizations and social movements mediate political preferences and policy agendas amongst the mass public? Second, it is interested in the collaboration between organizations and social movements and how these interactions shape traditional and untraditional forms of political participation. The study draws on a comprehensive mixture of quantitative (surveys, survey experiments, voter data analysis, social media analysis, and social network analysis) and qualitative (ethnographic observations, content analysis, elite interviews, and focus groups) methodological approaches to answer these questions. This study examines political activities during two electoral periods in several transformative states and municipalities. The broader impacts of the study are numerous. First, it connects a network of scholars from a diverse set of institutions. The project builds critical infrastructure at partner institutions to facilitate data collection and analysis. Namely, it (1) builds mobile research labs designed to conduct rapid response surveys during protests and organizational rallies, and (2) establishes data analysis centers at two minority serving institutions, and (3) provides cutting-edge training, tools, and professional resources to students from marginalized and underserved groups.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10173", "attributes": { "award_id": "2131959", "title": "3rd Enterprise and Infrastructure Resilience Workshop", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "Proc Sys, Reac Eng & Mol Therm" ], "program_reference_codes": [], "program_officials": [ { "id": 9480, "first_name": "Catherine", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2022-07-31", "award_amount": 13195, "principal_investigator": { "id": 26096, "first_name": "Debalina", "last_name": "Sengupta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26095, "first_name": "Izabela", "last_name": "Balicka", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 816, "ror": "https://ror.org/02yjcxz86", "name": "American Institute of Chemical Engineers", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The engineering systems designed today are increasingly complex and dependent on other surrounding systems. Due to this complexity and interdependence, it is often impossible to predict all disruptive events that can be expected in the system’s lifecycle, which may even lead to economically impactful disruptions and critical failures. Resilience is a concept encompassing various disciplines, and the creation of a resilient system will enable the system to recover from unpredicted disturbances and adapt to these new conditions. Efforts to improve resilience of chemical supply chains, chemical plants and energy networks will benefit society in by providing techniques and approaches that are needed to quickly respond to rapidly changing conditions. Knowledge from topics covered in this workshop is expected to improve our economy's and society's ability to recover from or adapt to large scale disasters, pandemics, stronger hurricanes, and deep freeze from winter storms. Further, these discussions will emphasize sustainability and safety throughout development. This virtual workshop will be advertised to a diverse audience. The NSF funds will be used to support student, post-doctoral fellow, and early-career researcher attendance, for those who would not otherwise be able to afford the registration. \n\nIncorporating concepts from resilience engineering in the design of systems such as chemical plants, chemical supply chains, and energy networks would be very effective in minimizing undesired effects of unforeseen disturbances yet requires expertise from various disciplines in science and engineering such as chemical engineers, sustainability engineers, mathematicians, and more. This workshop explores multifaceted resilience strategies for the modern enterprise that address dependence on external systems, such as the environment, stakeholders, shareholders, and society. The workshop would bring together professionals from various fields that are working in academia, industry, and government to have a wide range of perspectives. Such a combination of attendees has the potential of fostering impactful discussions and potential collaborations that would serve to further advance the field. Topics to be integrated at this workshop include risk management, optimization, operations research, sustainable engineering, critical infrastructure, process supply chains, computational models, machine learning methods, multi-scale systems analysis tools, and process safety.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10197", "attributes": { "award_id": "2117282", "title": "MRI: Acquisition of a High-Performance Computer System to Support Research and Training in Computational Biology and Data Science at Meharry Medical College", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Major Research Instrumentation" ], "program_reference_codes": [], "program_officials": [ { "id": 1343, "first_name": "Marilyn", "last_name": "McClure", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 671411, "principal_investigator": { "id": 26144, "first_name": "Aize", "last_name": "Cao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26143, "first_name": "Qingguo", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 938, "ror": "https://ror.org/00k63dq23", "name": "Meharry Medical College", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "This MRI aims to acquire a High-Performance Computing (HPC) System to support research and training in Computational Biology and Data Science in a Historically Black College and University (HBCU). The system will allow Meharry Medical College to: \n• Support and expand multidisciplinary research in big data and areas with high Computational needs; support Maharry’s planned school of Applied Computational Sciences by incorporating state-of-the-art \ngenetics studies, learning, data visualization, and other functions requiring high performance computing, and other functions, and other areas with high computational needs, \n• Assist in recruiting and retaining historically underrepresented minority students in computational sciences and biology, \n• Expand educational and research outreach in High Performance Computing in Middle Tennessee and other HBCU. \nThis HPC system aims at providing robust computational processing to support programs at the Data Science Institute. \n\nThis system will provide significant computational support for high throughput research and advance knowledge in computational biology and data science. This includes, but is not limited to big data management, sequencing data analyses, image processing, machine learning, and other types of data analyses (sequencing data and Acade analyses). The system offers a safe place for resource sharing, and contributes in retaining valuable knowledge, as well as providing opportunities for collaboration and engaging in trying to restrain the pandemic, and attaining more students.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10213", "attributes": { "award_id": "2150281", "title": "REU Site: Electronic Materials Evaluation Research for Greater Exposure to Future Technology Careers (EMERGE)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "XC-Crosscutting Activities Pro" ], "program_reference_codes": [], "program_officials": [ { "id": 9897, "first_name": "Robert", "last_name": "Meulenberg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2025-02-28", "award_amount": 389870, "principal_investigator": { "id": 26169, "first_name": "Chadwin", "last_name": "Young", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26168, "first_name": "Rashaunda", "last_name": "Henderson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).\n\nNon-Technical Abstract\n\nThe global pandemic and dwindling computer chip manufacturing here in the United States has impacted the production of chips. This has national security and economic implications that could impact US citizens’ livelihood. There is bipartisan support through pending legislation to bring back and reinvigorate computer chip manufacturing here in the United States to ensure a global competitive advantage and further enhance national security. To keep pace with this growth, an increase in a diverse, innovative, and globally competitive workforce is a necessity. Meanwhile, there has always been a need to increase the representation of underserved groups in the science, technology, engineering, and math (STEM) fields. Therefore, this Research Experience for Undergraduates (REU) site at the University of Texas at Dallas will establish an engaging exploration into the research methods for the evaluation and fundamental understanding of electronic materials and devices for use in energy, electronics, nanotechnology and sensing/detecting. Ten undergraduate Fellows will engage in an immersive 10-week summer research program focused on technologically relevant materials and analytical techniques for future innovations that will spark the next generation of talent for the high-tech industry. The program will also provide exposure to and guidance on graduate education, research methods, and research careers. This REU program will serve the state and surrounding region through undergraduate research opportunities to first-generation, underrepresented minorities and women scholars that attend Historically Black Colleges and Universities (HBCUs), Minority Serving Institutions (MSIs) in Texas and surrounding states, and DFW community colleges. The desire is for these students to pursue graduate studies and careers in advanced computer chip manufacturing and technology fields as the US works to re-establish this critical area in America.\n\nTechnical Abstract\n\nThis REU site at the University of Texas at Dallas will establish engaging fundamental research exploration in modeling, fabrication, and characterization of materials and devices. This research has implications on materials understanding for use in neuromorphic, nanophotonic, optoelectronic, ferroelectric, perovskites, carbon nanotubes, and quantum transport at the nanoscale for use in energy, electronics, nanotechnology, microfluidics, low-temperature device fabrication, and sensing/detecting fields. Ten undergraduate Fellows will engage in an immersive 10-week summer research program that will spark the next generation of talent for the high-tech industry. This Program includes: material from the PI’s undergraduate research methods course (e.g., research fundamentals, design of experiments, publishing, etc.), preparing for and applying to graduate school, specific training for the Fellow’s summer research, informal research seminars from faculty and outside experts, visits to local high tech industries, and seminars on ethics. This REU program will serve the state and surrounding region through undergraduate research opportunities to first-generation, underrepresented minorities and women scholars that attend HBCUs, MSIs in Texas and surrounding states, and Dallas Fort Worth community colleges. The desire is for these students to pursue graduate studies and careers in advanced semiconductor manufacturing and technology fields as the US works to re-establish this critical area in America.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10165", "attributes": { "award_id": "2111278", "title": "Collaborative Research: Particles and Proxies for Sampling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 6669, "first_name": "Yuliya", "last_name": "Gorb", "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": 149687, "principal_investigator": { "id": 26083, "first_name": "Gideon", "last_name": "Simpson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project addresses sampling in high dimensions which is important for a variety of disciplines, including computational chemistry, materials science, and molecular dynamics simulations for climate models, power network, traffic models, or the study of viruses and pandemics. The project will develop new simulation algorithms as well as improvements of existing algorithms. The outcomes will benefit these disciplines in several ways. First, the algorithmic optimizations will provide new tools that practitioners could use to accelerate their computations. Second, rigorous results on these methods will provide practitioners with confidence in their predictions. Finally, open source software will be developed. Students will be involved and receive interdisciplinary training.\n \nThe project addresses challenges in sampling and related problems arising from complex energy landscapes such as in potential energy in an atomistic system; the negative log-likelihood in a Bayesian inference problem; or the loss function in a machine learning problem. In Markov Chain Monte Carlo methods, these landscapes often define the evolution of a Markov chain that samples some target distribution. This project will develop efficient computations of ergodic averages over Markov chains and methods that reduce the computational cost of ergodic averages, by either reducing the number of required iterations or reducing the per-iterate cost. The new techniques and analyses will be based on proxy landscapes and interacting particle systems. Proxies can reduce per-iterate cost or lead to faster convergence, while interacting particle systems can reduce the bias from proxies or cut down on variance. The project includes a study of how parameter choices affect the variance of the weighted ensemble particle method at finite particle number; the development of a weight-corrected particle system to account for bias from proxies; and an analysis of methods for overcoming sampling difficulties associated with rough landscapes.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10205", "attributes": { "award_id": "2121410", "title": "RII Track-2 FEC: Genome Engineering to Sustain Crop Improvement (GETSCI)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director", "EPSCoR Research Infrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 11391, "first_name": "J.D.", "last_name": "Swanson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2025-09-30", "award_amount": 3993756, "principal_investigator": { "id": 26160, "first_name": "Michael", "last_name": "Muszynski", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26156, "first_name": "Jianming", "last_name": "Yu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26157, "first_name": "Zhi-Yan", "last_name": "Du", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26158, "first_name": "Teresita D", "last_name": "Amore", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26159, "first_name": "Keunsub", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 684, "ror": "", "name": "University of Hawaii", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true }, "abstract": "Improved and practical crop breeding tools are required to meet the increasing demands of a growing global population and to overcome the sudden and variable stresses, made worse, by climate change. This project brings together researchers from the University of Hawai’i at Manoa and Iowa State University to develop an efficient, robust genome engineering toolkit that can be used to speed the generation of resilient crops adapted to a changing environment. Reproductive barriers are a major bottleneck that limits the genetic diversity available for crop improvement. Tropical maize germplasm is a rich source of genetic diversity but its flowering behavior in temperate regions precludes its broad use for maize improvement. To access this diversity, our two institutions formed a collaboration that integrates our strengths in tropical plant biology and transformation (Hawai’i) with maize transformation, genome engineering, and breeding (Iowa). Our goals are to establish a rapid and efficient genetic transformation platform and to develop improved genome editing tools to reprogram the flowering behavior of high-yielding tropical maize lines allowing their incorporation into any maize breeding program. Both Hawai’i and Iowa will gain a valuable new capability in genetic transformation and genome engineering which will transform the types of crop research possible at both institutions. Expected impacts from this project will help address food security and economic weaknesses in Hawai’i, by allowing for the development of new tropical crop breeding industries. In Iowa, access to gene-edited temperate-adapted tropical germplasm will move maize improvement into the next era of genome-optimized breeding. Workforce capacity will be increased by engaging underrepresented students, particularly Native Hawai’ians and Pacific Islanders, in diverse aspects of genome engineering research, by the exchange of undergraduates between partner institutions to prepare a globally competitive, multiculturally, and socially responsible workforce, and by creating opportunities for improved science communication skills through training sessions, workshops, and engagement with the community to communicate the value and safety of these new tools. \n\nCritical to our future is maintaining the rate of genetic improvement of the crops that feed us and sustain our economy. But the sudden and increasingly severe stresses caused by climate change limit the pace of improvement. Advances in genome engineering offer rapid solutions by enabling precise and targeted reprogramming of molecular networks to improve crop performance. The rich genetic diversity in tropical maize is largely underutilized for maize improvement because tropical lines are photoperiod sensitive and flower late in the long-days of temperate growing regions. To access this diversity, we formed a collaboration between the University of Hawai’i at Manoa (UH Manoa) and Iowa State University (ISU), which integrates strengths in tropical plant system biology and transformation (UH Manoa) with maize transformation, genome engineering, and breeding (ISU). Our goal is to use gene editing to suppress the photoperiod response in elite, high-yielding tropical maize to promote earlier flowering. These edited tropical lines can then be used to enhance any maize breeding program. Our objectives are to (1) establish an efficient, germplasm-independent maize transformation platform, (2) develop a facile, tractable genome editing toolkit to suppress the photoperiod response in six tropical inbreds, (3) analyze photoperiod network function in genome edited tropical lines, and (4) improve skills in communicating the value and safety of these new genome engineering tools. \nThe outcomes from this project include new tropical maize transformation capabilities at both jurisdictions, genome editing reagents for modulating flowering in maize, six elite tropical inbreds adapted to temperate breeding programs, a mechanistic understanding of the response to reprogramming the flowering network, and improved skills to communicate the value of this technology in professional and public contexts. Broader impacts expected from this project include opening this technology to academic labs, that can build research capacity by allowing genome engineering of diverse crops. Democratizing these tools are expected to speed breeding advancements, sustain crop improvement efforts, and spur economic growth. Both Hawai’i and Iowa will gain a valuable new capability in maize transformation and genome engineering, and will transform the types of crop research possible at both institutions. In Hawai’i, this project will help address food security and economic weaknesses revealed by the pandemic, by allowing for development of new tropical crop breeding industries. In Iowa, access to gene-edited temperate-adapted tropical germplasm moves maize improvement into the next era of genome-optimized breeding. Workforce capacity will be increased by engaging underrepresented students, particularly Native Hawai’ians and Pacific Islanders, in diverse aspects of genome engineering research, by the exchange of undergraduates between partner institutions to prepare a globally competitive, multiculturally, and socially responsible workforce, and by creating opportunities for improved science communication skills through training sessions, workshops, and engagement with the community to communicate the value and safety of these new tools.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10157", "attributes": { "award_id": "2109745", "title": "US-China Collab: Comparative evolution and ecology of swine influenza viruses in China and the United States", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Ecology of Infectious Diseases" ], "program_reference_codes": [], "program_officials": [ { "id": 923, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2026-08-31", "award_amount": 2499991, "principal_investigator": { "id": 26072, "first_name": "Xiu-Feng", "last_name": "Wan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25720, "first_name": "Richard J", "last_name": "Webby", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25727, "first_name": "Michael E", "last_name": "Emch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 292, "ror": "", "name": "University of Missouri-Columbia", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Influenza A viruses are responsible for substantial human morbidity and mortality and continue to present an overwhelming public health challenge. It has been proposed that pigs are intermediate host “mixing vessels” that generate pandemic influenza strains through genetic reassortment among avian, swine, and/or human influenza viruses. Although evolutionary events (i.e., reassortment and mutations) have been routinely detected in swine population, it is not yet clear which are typical, which are atypical, which evolutionary events for these influenza viruses increase threats to human and animal health, and which ecological and evolutionary principals are driving such events. The overall goal of this study is to develop and apply interdisciplinary approaches to study and compare the evolution and ecology of swine influenza A viruses through synergistic studies in China and the US, the two largest pork producing countries on the planet, by assembling an international and multi-disciplinary team. Specifically, this project will 1) identify and determine the evolutionary dynamics of novel swine influenza viruses in swine populations in the two countries through influenza surveillance and advanced evolutionary analyses, 2) determine unique, common, and synergistic ecological drivers through geospatial modeling and machine learning, and 3) develop an influenza risk assessment tool using Big Data and Artificial Intelligence. This project will train graduate, undergraduate, veterinary, and medical students in interdisciplinary research skills for studying evolutionary biology, disease ecology, epidemiology, geospatial modeling, Big Data, and AI. Through internship and outreach activities, this project will also educate the public and non-academic stakeholders on ecology and evolution and transmission of infectious diseases, which may lead to the optimization of swine industry management and changes in human behaviors that could reduce the influenza evolutionary events in pigs, disease transmission among pig populations, and spillover of swine influenza virus to humans.\n\nThis study will illustrate the evolutionary dynamics of swine influenza viruses leading to enhanced zoonotic and pandemic risk and identify atypical evolutionary events by defining a baseline for influenza prevalence and evolution. It is expected that ecological drivers associated with emergence and spread of novel swine influenza viruses within swine populations and at the animal-human interface will be identified. In addition, data from two unique but linked ecological settings will be integrated using an interdisciplinary approach to facilitate the comprehensive understanding of the evolution and ecology of influenza A viruses within swine populations and at the animal-human interface. Furthermore, Big Data and AI-based computational tools will be developed and shared to advance computational methods linking medical, veterinary, social, and environmental sciences, enhancing our ability to respond to emerging and reemerging infectious diseases. This study aims to facilitate our understanding of the natural history of influenza viruses and advance ecological theories for influenza viruses. The knowledge from this study will help inform and optimize policies and countermeasures for influenza pandemic preparedness.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10181", "attributes": { "award_id": "2125196", "title": "SCC-PG: Online Role-Playing Games for Improving Multi-Stakeholder Collaboration in Concurrent Disaster Response Planning", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "S&CC: Smart & Connected Commun" ], "program_reference_codes": [], "program_officials": [ { "id": 915, "first_name": "Michal", "last_name": "Ziv-El", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2023-05-31", "award_amount": 149247, "principal_investigator": { "id": 26116, "first_name": "Divya", "last_name": "Chandrasekhar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26112, "first_name": "John D", "last_name": "Horel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26113, "first_name": "Robert M", "last_name": "Young", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26114, "first_name": "Masood", "last_name": "Parvania", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26115, "first_name": "Ivis Garcia", "last_name": "Zambrana", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 202, "ror": "https://ror.org/03r0ha626", "name": "University of Utah", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "Effective community-level post-disaster response and recovery requires that actions of all stakeholders involved in response be coordinated. But existing approaches to disaster response and recovery management typically underemphasize the role of such multi-stakeholder coordination, particularly the involvement of community residents. Literature also provides limited advice on how to address overlapping or concurrent disasters, especially where one of the disasters is a pandemic. This becomes problematic when local authorities must respond to multiple events at once; when response to specific disasters (e.g., pandemics) is siloed; and when disaster concurrency exacerbates disproportional impact on socially vulnerable community members. Effective and equitable response planning for ‘overlapping’ or ‘concurrent’ disasters, therefore, requires better and more effective understanding and this Smart and Connected Communities Planning Grant (SCC-PG) study advances the NSF’s mission to promote the progress of science by generating scientific knowledge of factors affecting disaster response actions of diverse community stakeholders in the face of multiple hazards. This study also advances knowledge of the role and value of inter-stakeholder collaboration in disaster response planning as well its success factors. Lastly, this study advances NSF’s mission to promote national health, prosperity, and welfare of local communities by focusing on disaster response in the Intermountain West, which is at significant risk from fast and slow-onset disaster events such as wildfires, extreme heat events, earthquakes, and climate change.\n\nThis SCC-PG project employs community engagement techniques, qualitative inquiry methods and table-top role-playing games (RPGs) to lay the foundation for development of an online multi-player AI-mediated RPG to improve inter-stakeholder collaboration in response planning for concurrently occurring disasters. The project examines four research topics: i) factors affecting response decisions of various community stakeholders (such as residents, non-profits, government, and utility providers); (ii) types of information, data or communication structures needed to improve inter-stakeholder interaction and collaboration for response; (iii) effect of information exchange and collaboration on response decisions made at the individual and collective level; and (iv) characteristics of effective and equitable communication or collaboration structures for multi-stakeholder response planning for concurrent disasters. The results of this study and the subsequent SCC-IRG research will help any community undertaking disaster response planning to identify response actions that are at once more equitable, can work simultaneously for pandemics and other disasters, and integrate social and infrastructural dimensions.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1, "pages": 1392, "count": 13920 } } }