Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=4&sort=keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1397&sort=keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=5&sort=keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=keywords" }, "data": [ { "type": "Grant", "id": "1536", "attributes": { "award_id": "2029774", "title": "RAPID: Comparative genomics of SARS-CoV-2 susceptibility and immune defense in mammals", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7914", "9179" ], "program_officials": [ { "id": 4007, "first_name": "Joanna", "last_name": "Shisler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 199767, "principal_investigator": { "id": 4009, "first_name": "Elinor K", "last_name": "Karlsson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 613, "ror": "https://ror.org/0464eyp60", "name": "University of Massachusetts Medical School", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4008, "first_name": "Diane P", "last_name": "Genereux", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 613, "ror": "https://ror.org/0464eyp60", "name": "University of Massachusetts Medical School", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The goal of this project is to compare genomes of hundreds of mammal species, finding differences in DNA that distinguish species resistant to SARS-CoV-2 from those that are very susceptible. This information is needed to understand how the current SARS-CoV-2 virus spread to humans and to identify potential host animals (e.g., pet, livestock, and pest species) that may be susceptible to SARS-CoV-2 in the USA. SARS-CoV-2, the cause of the COVID-19 pandemic, can infect diverse species of animals. There is a variation in susceptibility to and severity of disease between species. This variation suggests that some species have genetic differences that dictate susceptibility to COVID-19. This work will identify how coronaviruses adapt to new host species, information that will help predict and control future coronavirus outbreaks. Funding will support training a graduate student in research, thereby training the next generation of the bioeconomy workforce. This project will investigate how the host genome shapes host-pathogen interactions, and how coronaviruses like SARS-CoV-2 evolve to exploit new hosts. The researchers will compare existing genomic data for hundreds of mammals using three complementary approaches: (1) Measure structural and sequence homology in two host proteins, ACE2 and TMPRSS2, necessary for infection in humans; (2) Analyze existing RNA-seq datasets to (a) identify species with co-expression of ACE2 and TMPRSS2, and potentially other proteases implicated in infection, in the same tissue, and (b) search for incidental coronaviral sequence data from diverse mammalian species; (3) Test for variants in evolutionarily conserved elements that are correlated with species susceptibility, using forward genomics. With these analyses, the researchers will identify species with potential as reservoirs for SARS-CoV-2 viral spillback into humans, and those that are promising systems for investigating SARS-CoV-2 evolution, host defenses, and host-pathogen interactions. This RAPID award is made by the Physiological and Structural Systems Cluster in the BIO Division of Integrative Organismal Systems, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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": "768", "attributes": { "award_id": "2050640", "title": "Planning Virtual Strategies to Prepare Science and Mathematics Teachers in Mississippi", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 1805, "first_name": "Susan", "last_name": "Carson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2023-02-28", "award_amount": 124992, "principal_investigator": { "id": 1809, "first_name": "Mitchell M", "last_name": "Shears", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 396, "ror": "https://ror.org/01ecnnp60", "name": "Jackson State University", "address": "", "city": "", "state": "MS", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1806, "first_name": "Abu O", "last_name": "Khan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1807, "first_name": "Alicia K", "last_name": "Jefferson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1808, "first_name": "Nadine", "last_name": "Gilbert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 396, "ror": "https://ror.org/01ecnnp60", "name": "Jackson State University", "address": "", "city": "", "state": "MS", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national need for skilled secondary science and mathematics teachers in high-need school districts. To do so, the project seeks to lay the foundation for secondary-education certification programs adapted to the novel demands of pre- and post-COVID teaching/learning environments. Conceived initially as a response to the COVID-19 pandemic, the project aims to use technology and virtual approaches to deliver remote learning opportunities for future teachers. As such, the project will enable Jackson State University to explore the feasibility of a large-scale effort to increase use of evidence-based, distance-learning strategies in teacher education. Examples of strategies include virtual simulations, digital credentialing, and online social and emotional learning. The work will be situated in the urban setting of the Mississippi State capital. This project at Jackson State University includes partnerships with Hinds Community College and Jackson Public Schools, a high-need school district. The long-term goal of this collaborative effort is plan how to recruit, support, and graduate teachers who will help meet the shortage of science and mathematics teachers at high-need schools often staffed by rotating long- and short-term substitute teachers. The project builds on the conceptual framework of Jackson State’s College of Education and Human Development vision of the “responsive educator” who provides and embodies: 1) a Committed Response; 2) a Knowledgeable Response; 3) a Skillful Response; and 4) a Professional Response. Additionally, the project builds on the current infrastructure of the University’s Physics and Mathematics Education curriculum. The goals of this Capacity Building project are to: 1) develop evidence-based innovative models and strategies for recruiting, preparing, and supporting teachers; 2) create plans for collecting data to determine need, interest, and capacity for increasing STEM teacher development; and 3) establish the infrastructure for preparing a Track 1: Scholarship & Stipend proposal in the future. This Capacity Building project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the persistence, retention, and effectiveness of K-12 STEM teachers in high-need school districts.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": "3584", "attributes": { "award_id": "1654828", "title": "Collaborative Research: The Impact of Face-to-Face and Remote Interviewing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "LSS-Law And Social Sciences" ], "program_reference_codes": [], "program_officials": [ { "id": 11632, "first_name": "Reginald", "last_name": "Sheehan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2017-05-01", "end_date": "2021-04-30", "award_amount": 145095, "principal_investigator": { "id": 11634, "first_name": "Debra", "last_name": "Poole", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1001, "ror": "https://ror.org/02xawj266", "name": "Central Michigan University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 11633, "first_name": "Christopher", "last_name": "Davoli", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1001, "ror": "https://ror.org/02xawj266", "name": "Central Michigan University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite widespread dissemination of best-practice standards for conducting forensic interviews, many jurisdictions lack the expertise to skillfully investigate crimes involving child witnesses. An efficient way to ensure that all jurisdictions have access to highly trained child interviewers is to conduct remote (live-streaming video) forensic interviews. Remote interviewing could reduce investigative response time, spare investigative resources, and accelerate case disposition. However, the ability of remote interviewing to elicit eyewitness evidence from children has not been sufficiently tested and, therefore, will certainly prompt challenges regarding children?s testimonial reliability. The current project is a comprehensive and theoretically grounded evaluation of the effectiveness of remote interviewing of child witnesses. Results will be disseminated to scientists and forensic professionals through publications and presentations, thereby informing policies and guidelines for the use of remote forensic interviews with children. Because remote interviewing increases access to specialized expertise, project results will also impact how children are questioned by electronic means in non-forensic contexts. The project will provide research training to dozens of students at two research sites and promote greater awareness of evidence-based practice through outreach to practitioners who work with child witnesses. \n\nUsing an established paradigm that produces salient touching experiences, individual children at two sites (ages 4 to 8 years) will be told that a male assistant can no longer touch their skin when he delivers a germ education program. The assistant will touch each child once and realize an impending mistake before he completes a second touch. Afterward, children will hear a story from their parents that contains misinformation about the experience, including narrative about a nonexperienced touch. During interviews conducted in traditional face-to-face or remote formats, children will answer questions about the germ education event and answer a series of questions that tests their ability to distinguish experienced from suggested events. By comparing the completeness and accuracy of children?s testimonies across formats, this study will determine whether remote interviewing elicits testimony that is comparable in quality to the testimony elicited by face-to-face interviewing. Measures of behavioral inhibition and executive function will determine whether remote interviewing is beneficial for children who are behaviorally inhibited or contraindicated for typically-developing children who have poor cognitive control.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1281", "attributes": { "award_id": "2027744", "title": "RAPID/Collaborative Research: High-Frequency Data Collection for Human Mobility Prediction during COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "036E", "042E", "096Z", "1576", "7914" ], "program_officials": [ { "id": 3296, "first_name": "Daan", "last_name": "Liang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 22399, "principal_investigator": { "id": 3297, "first_name": "Qi", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 has and is continuing to dramatically alter the lives of millions of Americans as businesses, schools, and many public places have closed around the country. Recommendations of public officials along with individual concerns and fears have fundamentally changed the pattern of daily routines as Americans have adopted the practices of social distancing, sheltering in place, and even self-quarantine. This Rapid Response Research (RAPID) project will improve our ability to assess and predict changes in mobility patterns under sudden disruptions caused by large-scale public health crises such as COVID-19. The specific focus will be to understand changes in mobility patterns and the complex and dynamic decision-making process shaping these changes during the unfolding events associated with this major public health crisis. The project will advance the national health, prosperity, and welfare by greatly improving the preparedness and responses of public agencies facing COVID-19 and future similar public health crises. It will also help understand and predict reduction, change, and recovery of human mobility patterns promoting the progress of science in human mobility and urban resilience, in alignment with the mission of NSF.The objectives of this RAPID project are to: (1) capture and ultimately predict spatiotemporal changes in the patterns of human mobility in response to the COVID-19 pandemic using social media data mining techniques; (2) perform high-frequency individual-level surveys via a smartphone app to understand motivational, decisional, and sentimental factors shaping changes in mobility patterns; and (3) explore conversion and convergence functions for high fidelity and high accuracy human mobility prediction. The intellectual merits of this research include: the discovery of unique mobility patterns emerging from this public health crises related to social distancing, sheltering, and self-quarantine practices; the unprecedented gathering of longitudinal evidence about the motivational, decisional and sentimental factors shaping mobility decisions; and the development of innovative algorithms of using a small representative sample for high-fidelity mobility prediction. The data and knowledge gained from the project will enhance future studies on urban mobility, travel demand and resource allocation modeling, and help policymakers assess the response and recovery of major urban metropolitan area facing a devastating disaster such as COVID-19. Project outcomes will be disseminated through the Boston Area Research Initiative (BARI), an inter-university partnership between Northeastern University and Harvard University, and through the MetroLab Network.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": "13824", "attributes": { "award_id": "2111403", "title": "Collaborative Research: Integrating Perspective-taking and Systems Thinking for Complex Problem-Solving", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 1869, "first_name": "Thomas", "last_name": "Kim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-15", "end_date": null, "award_amount": 372888, "principal_investigator": { "id": 30167, "first_name": "Rebecca", "last_name": "Jordan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30167, "first_name": "Rebecca", "last_name": "Jordan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 521, "ror": "https://ror.org/05hs6h993", "name": "Michigan State University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by using computer modeling to present multiple perspectives, helping students to improve systems thinking and complex socio-technical problem-solving. The topics with which STEM fields grapple are often not merely scientific problems; they are also at times ideologically and emotionally charged issues. Such contemporary “socio-scientific” issues affect multiple stakeholders in different ways and have real and diverse consequences, both economic and societal. Understanding and responding to these socio-scientific issues, such as genetically modified crops, vaccine development/deployment and climate change mitigation, requires that individuals not only understand scientific content and how systems work, but also how these systems look from different vantage points. The goal of this project is to develop and assess new teaching strategies to improve understanding and decision-making related to such complex social and environmental problems. This project will build on research from previous studies to develop the state-of-the-art undergraduate instruction strategies. The software, curricular tools, and case studies being designed will be used by college instructors across the United States to promote perspective-taking and problem-solving. Formally engaging students in this type of thinking is essential to the training of America’s future workforce. <br/><br/>Specifically, students will engage with a series of case studies on complex socio-scientific issues, and use the MentalModeler (www.mentalmodeler.org) software to map out the system from the perspectives of different stakeholders. We believe this approach will promote systems thinking, model-based reasoning, perspective taking, and problem-solving ability in the undergraduate classroom. We intend to test the hypothesis that integrating novel perspective taking and systems modeling across different undergraduate courses: 1) helps students overcome some of the cognitive and motivational obstacles elicited by controversial socio-scientific topics, 2) leads to a deeper and more accurate understanding of the system itself, and 3) helps learners create and support arguments regarding the effect of interventions on system-level outcomes. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12800", "attributes": { "award_id": "2302813", "title": "Collaborative Research: Adaptable Game-based, Interactive Learning Environments for STEM Education (AGILE STEM)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Cyberlearn & Future Learn Tech" ], "program_reference_codes": [], "program_officials": [ { "id": 1414, "first_name": "Soo-Siang", "last_name": "Lim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-15", "end_date": null, "award_amount": 350000, "principal_investigator": { "id": 28719, "first_name": "Daniell", "last_name": "DiFrancesca", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 28719, "first_name": "Daniell", "last_name": "DiFrancesca", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Learners of all ages are expected to be prepared to interact with emerging and technology-driven work environments. In addition, the growing reliance on online learning and its unprecedented and unexpected acceleration due to the COVID-19 pandemic are expected to change the education landscape forever. Thus, there is a need to grow the development of digital platforms for teaching and learning. Emerging technologies such as machine learning and high fidelity simulated environments have the potential to create customized and adaptable learning environments to support STEM learning outcomes. This project serves the national interest by advancing the knowledge about designing and creating adaptable game-based, interactive learning environments for STEM. The inclusion of underrepresented minority and female learners in the design stages of these learning environments, their portability, as well as the capability of these environments to be customized and adaptive have the potential to enhance education equality, engagement, and learning outcomes, and broaden their usability to several STEM domains. Moreover, the narratives and simulation models are inspired by real-world systems. Therefore, the learning environments are expected to enhance the learner’s understanding of complex system concepts that are challenging to understand using traditional teaching approaches and will help build the much-needed skills for the U.S. future STEM workforce. The proposed emerging technologies do not necessarily need access to specialized equipment, which eliminates barriers to scalability and border implementation and use. <br/><br/>The primary goals of this project are to automatically customize and adapt three-dimensional (3D) simulated game-based learning environments to improve engagement, and provide a deeper understanding of their design, development, and deployment, impact on learning and self-regulated learning (SRL) skills, and knowledge transferability from the learning environments to real-life applications. The project addresses the lack of scientific evidence and/or work in the following thrust areas: 1) the potential of reducing the barriers to content generation of 3D simulated game-based learning environments using emerging and advanced machine-learning methods; 2) creating customized content and adaptive 3D simulated game-based learning environments that improve and maintain learners motivation and engagement, enhance learning via instructional assistive content scaffolding, and increase knowledge transferability from game to real-life applications; 3) assessing the effectiveness of the learning environments for all learner groups in online and residential settings; and 4) exploring how learner decision-making and behavior data in the simulated game-based learning environments, and eye-tracking, facial expressions, bio-signals, and usage data, enhance knowledge about the relationships between decision-making/usage and SRL skills development.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9472", "attributes": { "award_id": "6U48DP006382-02M002", "title": "Connecting Behavioral Science to COVID-19 Vaccine Demand (CBS-CVD) Network Supplement for PRC - Increasing Effective Mental Health Care for LGBT Clients", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2019-09-30", "end_date": "2024-09-29", "award_amount": 500000, "principal_investigator": { "id": 25187, "first_name": "BRADLEY O", "last_name": "BOEKELOO", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1021, "ror": "", "name": "UNIV OF MARYLAND, COLLEGE PARK", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1021, "ror": "", "name": "UNIV OF MARYLAND, COLLEGE PARK", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": null, "keywords": [], "approved": true } }, { "type": "Grant", "id": "13312", "attributes": { "award_id": "2149848", "title": "Developing the thermodynamic solid solution models for Th, U, REE phosphates needed to identify the formation conditions of Th, U-depleted REE ores", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Petrology and Geochemistry" ], "program_reference_codes": [], "program_officials": [ { "id": 2399, "first_name": "Rachel", "last_name": "Teasdale", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 489, "ror": "", "name": "Chico State Enterprises", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2022-03-01", "end_date": null, "award_amount": 425742, "principal_investigator": { "id": 29391, "first_name": "Xiaofeng", "last_name": "Guo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 306, "ror": "https://ror.org/05dk0ce17", "name": "Washington State University", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Rare earth elements (REE) are critical for the future of the U.S. economy, renewable energy, and national security. New technologies enhancing environmental sustainability, defense capability, and consumer products have sharply increased the demand for the REE. However, the supply of REE in the U.S. relies mostly on their import from foreign sources. Furthermore, many of the domestic deposits suffer from high concentrations of thorium (Th) and uranium (U) that contaminate the environment during mining. To deal with this issue, it is necessary to discover the conditions needed for economic concentrations of the REE to form in nature in the absence of Th or U; this requires understanding how Th and U mix with the REE in minerals and how such mixing can be avoided. This project will explore how Th and U mix with the REE in minerals through a combination of experiments and geological modeling. These results will make it possible to predict the natural environments in which Th- and U-poor REE minerals form and thereby provide geologists with the information they need to develop strategies to explore for and locate deposits of REE that can be economically and safely mined. This project also aims to educate high school, undergraduate, and graduate students in geochemistry, and prepare them for careers as scientists. The integrated education plan is committed to holding summer geochemistry schools for high school students, promoting geochemical education to student through visits to national laboratories and virtual lectures, and engaging students in the experimental and modeling methods that are used in the research project.<br/><br/>The objective of this research proposal is to generate new knowledge enabling identification of the conditions under which Th and U-depleted REE phosphate ores can form. One of the major impediments to the recovery of REE from ores in the U.S. is the radioactivity generated during their processing and refining due to the presence of high concentrations of Th and U in the main REE ore minerals, monazite and xenotime. Thus, there is a strong need to find Th, U-depleted REE ores, which, in turn, depends on developing a better understanding of their formation conditions (e.g., temperature, pressure, oxygen fugacity, pH, etc.), particularly those for which the incorporation of Th and U into phosphate structures is minimal. Whereas the properties of the aqueous species of the REE, U, and Th are now reasonably well-known for the hydrothermal conditions of REE ore formation, there is almost no information on phosphate-based REE/U/Th solid solutions. A major challenge is to correctly account for thermodynamic non-ideality due to the mixing of Th and U with the REE, which is often incorrectly assumed to be ideal and thus can lead to inaccurate or false predictions. This project will establish accurate thermodynamic models describing the incorporation of Th and U in REE phosphates that are critically needed by geochemical modelers to predict the mobilization, fractionation, and deposition of REE/U/Th in hydrothermal systems. The knowledge obtained will enable the development of new exploration techniques permitting the identification and localization of Th- and U-depleted REE ores. The project also offers unique opportunities for students to receive the interdisciplinary education and training needed to become geochemists with broad mindsets and skillsets. This includes summer geochemistry schools for high school students and teachers to develop their interest in the geochemistry of the REE and other critical metals.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "3328", "attributes": { "award_id": "1811163", "title": "Advancing the Design of Visualizations for Informal Science Engagement", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "AISL" ], "program_reference_codes": [], "program_officials": [ { "id": 10551, "first_name": "Chia", "last_name": "Shen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2018-10-01", "end_date": "2019-12-31", "award_amount": 249677, "principal_investigator": { "id": 10553, "first_name": "Jennifer", "last_name": "Frazier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1082, "ror": "https://ror.org/0037yf233", "name": "Exploratorium", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 10552, "first_name": "Joyce", "last_name": "Ma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1082, "ror": "https://ror.org/0037yf233", "name": "Exploratorium", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants. This project is a two-day conference, along with pre- and post-conference activities, with the goal of furthering the informal science learning field's review of the research and development that has been conducted on data visualizations that have been used to help the public better understand and become more engaged in science. The project will address an urgent need in informal science education, providing a critical first step towards a synthesis of research and technology development in visualization and, thus, to inform and accelerate work in the field in this significant and rapidly changing domain.\n\nThe project will start with a Delphi study by the project evaluator prior to the conference to provide an Emerging Field Assessment on data visualization work to date. Then, a two-day conference at the Exploratorium in San Francisco and related activities will bring together AISL-funded PIs, computer scientists, cognitive scientists, designers, and technology developers to (a) synthesize work to date, (b) bring in relevant research from fields outside of informal learning, and (c) identify remaining knowledge gaps for further research and development. The project team will also develop a website with videos of all presentations, conference documentation, resources, and links to social media communities; and a post-conference publication mapping the state of the field, key findings, and promising technologies. \n\nThe initiative also has a goal to broaden participation, as the attendees will include a diverse cadre of professionals in the field who contribute to data visualization work.\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": "12544", "attributes": { "award_id": "2229975", "title": "Collaborative Research: CyberTraining: Pilot: Operationalizing AI/Machine Learning for Cybersecurity Training", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28473, "first_name": "Houbing", "last_name": "Song", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 315, "ror": "", "name": "Embry-Riddle Aeronautical University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "The interplay between AI and cybersecurity introduces new opportunities and challenges in the cybersecurity of AI as well as AI for cybersecurity. However, operations and configurations of AI cyberinfrastructure (CI) with a security mindset are rarely covered in the typical AI curriculum. To fill this gap, this project intends to develop hands-on training materials and provide mentored training for current and future research workforce in engineering and science-related disciplines. By transforming and integrating training materials into a course curriculum, this project aims to train potential cyberinfrastructure professionals in the CI community at large to handle AI with and for cybersecurity. This project has the potential to develop the research workforce in operating AI cyberinfrastructure with a security mindset to meet the national and economical needs and priorities of CI advancement. This project’s goal is to broaden the adoption of advanced cyberinfrastructure through training. This project develops a holistic technical approach for cybertraining: to identify, apply, and evaluate AI techniques which are inextricably related to well-defined operational cybersecurity challenges. The project intends to develop a Docker-based training platform that simulates and pre-configures a variety of scenarios to support hands-on AI cyberinfrastructure operations in the context of cybersecurity. Three levels of projects (exploratory, core, and advanced) are designed and integrated into the platform to help researchers and educators customize and develop into different education and training environments. The project democratizes the access and adoption of advanced AI cyberinfrastructure, while integrating cyberinfrastructure skills with the security mindset to foster inter-disciplinary and inter-institutional research collaborations. In addition to the dissemination through publications and social media, the outcomes from this project have the potential to benefit the greater cyberinfrastructure community and beyond, through the training and the sharing of the \"AI for and with cybersecurity\" course curriculum. This project is jointly funded by OAC and the CyberCorps program.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": 4, "pages": 1397, "count": 13961 } } }