Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=-id
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This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award, each Agency funds the proportion of the budget and the investigators associated with its own investigators and component of the work. This project is to (1) operate, maintain, and expand a high-latitude array of autonomous instruments to support research of the wider geospace research community into the sources of inter-hemispheric asymmetries, (2) conduct focused science investigations to develop understanding of the sources of high-latitude magnetic perturbations in the multi-scale, global, solar wind - magnetosphere – ionosphere – ground (SWMIG) system, including during the 2021 solar eclipse and (3) conduct education and outreach to facilitate broader access to polar research efforts. These objectives will be achieved through an unsurpassed network of closely-spaced magnetically-conjugate magnetometers in Antarctica and in the Northern Hemisphere near the 40 degree magnetic meridian, most of which have already been deployed. This project expands an existing Virginia Tech/Technical University of Denmark partnership to include the British Antarctic Survey (BAS), Space Science Institute, and UCLA. Graduate and undergraduate students will be supported, including a special research program to engage students from minority-serving institutions.Measurements of surface magnetic field perturbations are important to remotely sense and characterize the SWMIG phenomena that affect technology – such as geomagnetically induced currents – and thereby to develop physical models and forecast space weather impacts. However, understanding the sources of magnetic perturbations in the coupled SWMIG system is challenging due to their simultaneous dependence on driving conditions, ionospheric conductivity and ground conductivity. We seek to address the following science questions, \"How do magnetosphere-ionosphere current systems couple to high-latitude ground magnetic perturbations? What roles do current system spatial scale, inhomogeneous ionospheric conductivity, and inhomogeneous ground conductivity play?\" By combining British Antarctic Survey, Technical University of Denmark, and NSF-supported magnetometers, a new combined array will provide unprecedented coverage throughout the auroral zone/cusp in both hemispheres simultaneously. These data enable novel experiments to isolate the respective contributions of driver spatial/temporal scale, ionospheric conductivity, and local ground conductivity in the generation of ground magnetic perturbations. This project includes field work in the Antarctic, supported by both the U.S. Antarctic Program (USAP) and the BAS. USAP and BAS have agreed to support maintenance visits to receiver site locations and to support the retrograde of equipment at the end of the program. BAS and USAP will work collaboratively to deploy an additional instrument to a logistically feasible location that best serves the project. The USAP and BAS have agreed to support this program logistically, with the first field deployment year to be determined after the uncertainties related to the coronavirus pandemic are resolved.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": "610", "attributes": { "award_id": "2005052", "title": "A Contextual Examination of Ethnic-Racial Identity and Critical Consciousness among Diverse College Students", "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": 1351, "first_name": "Josie Welkom", "last_name": "Miranda", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-06-30", "award_amount": 129000, "principal_investigator": { "id": 1354, "first_name": "Ursula E", "last_name": "Moffitt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 326, "ror": "", "name": "Moffitt, Ursula E", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1352, "first_name": "Margarita", "last_name": "Azmitia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1353, "first_name": "Leoandra O", "last_name": "Rogers", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 326, "ror": "", "name": "Moffitt, Ursula E", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Leoandra Onnie Rogers at Northwestern and Dr. Margarita Azmitia at University of California Santa Cruz, this postdoctoral fellowship award supports an early career scientist examining the role of the sociopolitical context in the development of ethnic-racial identity and critical consciousness among college-going young adults. The current sociopolitical climate is fraught and can be psychologically taxing for everyone, though its implications are more dire for some individuals than others. Since 2016, the number of reported hate crimes has risen sharply (FBI, 2018). On university campuses, violence against marginalized groups has also increased (Watt, Costa, & Quiason, 2018). In the wake of the coronavirus pandemic, racism against Asian Americans has spiked (Coates, 2020) and Black, indigenous, and other minoritized individuals are getting sick and dying at disproportionate rates (Dyer, 2020). A better understanding is needed of how young adults from diverse ethnic-racial and political backgrounds alternately endorse, accept, or resist such events in both the local and national contexts. How are their responses situated within their sense of identity and their views on the world? If young people engage with sociopolitical events online or in person, what does this look like and what meaning do they draw from it? This project investigates these questions, linking the micro-level of daily life with the macro-level of the sociopolitical climate, examining the development of college-going young adults’ identities, beliefs, and behaviors in context.This project focuses specifically on critical consciousness (CC), a construct originally developed by Paolo Freire (1970), which includes an awareness of and resistance to societal inequity. Both CC and clarity about one’s ethnic-racial identity (ERI) can buffer the negative effects of discrimination (Diemer, Rapa, Park, & Perry, 2016; Yip, Wang, Mootoo, & Mirpuri, 2019) and link to myriad positive outcomes, including academic achievement and wellbeing (El-Amin et al., 2017; Rivas‐Drake et al., 2014). ERI and CC have rarely been investigated in tandem, however, and their development remains understudied among college students (Mathews et al., 2019). This project addresses this empirical gap using a longitudinal, mixed-methods approach, with data collection occurring at four time points across the 2020-2021 academic year. Survey data will be analyzed using person-centered longitudinal transition analysis (LTA), first creating clusters based on responses to ERI and CC measures, then examining who is likely to be in which cluster, who changes, and how these changes relate to additional variables such as political conservatism and sense of belonging. Additionally, open-response prompts will be gathered for ten day periods, asking participants to describe their engagement with and make meaning about real-time events. Responses will be analyzed using narrative methods, illuminating within- and between-person differences. This mix of methods and analyses allows for a rich investigation of ERI and CC development in context, advancing theory and promoting equity-oriented outcomes among young people with diverse identities and experiences.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": "609", "attributes": { "award_id": "2016849", "title": "Automated Collaboration Assessment Using Behavioral Analytics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1348, "first_name": "Tatiana", "last_name": "Korelsky", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2023-09-30", "award_amount": 749976, "principal_investigator": { "id": 1350, "first_name": "Nonye M", "last_name": "Alozie", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 325, "ror": "https://ror.org/05s570m15", "name": "SRI International", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1349, "first_name": "Anirudh", "last_name": "Som", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 325, "ror": "https://ror.org/05s570m15", "name": "SRI International", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Automated Collaboration Assessment Using Behavioral Analytics project willmeasure and support collaboration as students engage in STEM learning activities.Collaboration promotes clarifications of misconceptions and deeper understanding of conceptsin STEM which prepares students for future employment in STEM and beyond. This projectaligns with the goal of the Cyberlearning for Work at the Human-Technology Frontier program tofund exploratory research that supports learners in working productively in technology-richSTEM environments. Collaboration is an important learning skill in K-12 STEM education, yet teachers have fewconsistent ways to measure and support students’ development in this area. This project willresult in both an improved understanding of productive collaboration and a prototypeinstructional tool that can help teachers identify nonverbal behaviors and assess overallcollaboration and engagement quality. Using nonverbal behaviors to assess engagement willdecrease dependence on discourse and content-based dialogue and increase the transferabilityof this work into different domains. This project is particularly timely as the ability to collaborateand engage in group work are growing requirements in professional and learning settings; at thesame time the very act of collaboration is being disrupted by the Coronavirus pandemic andthere is a high likelihood that much of this “new normal” (social distancing; combining in-personand remote collaboration) will be with us for some time. This project will meet the urgent needcurrently felt by educators and educational institutions to support the development ofcollaboration skills among students, even as the very act of collaboration is shifting and nontraditionalforms of education are taking hold.This project is a collaboration between the Center for Education Research andInnovation (CERI) and Center for Vision Technology (CVT) at SRI International (SRI) and willcapture multiple students’ actions as they work collaboratively face-to-face, both in-person andthrough a virtual platform. This project will use a collaboration conceptualmodel, multistage predictive and explainable machine learning models, and video analytics toassess and report on collaborative behaviors and interactions. The behavior analytics systemwill use facial expressions, body movements, and meta-information about the collaboration taskto identify interactions that show how students contribute to the collaboration, individually andcollectively. This 2-year project will use reliability and model prediction testing and sequential,correlation, and thematic analyses of video recordings, surveys, interviews, and student artifactsto answer the following research questions: Can machine learning models reliably assesscollaboration when compared to human assessments? How do individual behaviors duringcollaboration lead and relate to group level interactions and collaboration quality? and Can wevalidate and relate the assessed collaboration behaviors to student outcomes as represented bygroup-generated artifacts? The intellectual merits include contributions to the advancement oftwo fields: (1) machine learning— by developing and exploring new algorithms that generateexplainable collaboration skill assessments and teacher/student dashboards at different grainsizes of the interactions, and (2) learning sciences—by contributing a collaboration conceptualmodel that shows how specific skills, interactions, and behaviors correspond to collaborationquality at group and individual levels. Broader impacts of this work include increasing theavailability and types of feedback presented to instructors and learners from diversebackgrounds. This will expand the settings and number of individuals who can be evaluated andsupported on collaboration by making collaborative learning easier to monitor through tools thatcan be used by a wide audience of educators and professionals.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": "608", "attributes": { "award_id": "2013230", "title": "Using Virtual Reality Mathematics and Science Simulations to Prepare Elementary Teachers to Create Successful Learning Experiences for Students in High-Need Urban Schools", "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": 1345, "first_name": "Kathleen", "last_name": "Bergin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2023-09-30", "award_amount": 586111, "principal_investigator": { "id": 1347, "first_name": "Cheryl L", "last_name": "Ney", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 324, "ror": "", "name": "California State L A University Auxiliary Services Inc.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1346, "first_name": "Kimberly", "last_name": "Persiani", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 324, "ror": "", "name": "California State L A University Auxiliary Services Inc.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest of improving teaching and learning of mathematics and science. Specifically, it will investigate the use of mixed reality classroom simulations in the preparation of K-8 teachers in urban schools. Mixed-reality virtual simulations combine real people and physical environments with virtual people and places. Pilots spend long hours using virtual simulators before flying a plane carrying real passengers. It is now possible to provide K-8 pre-service teachers with opportunities to virtually practice teaching strategies that can enhance learning before serving students in real classrooms. California State Los Angeles University Charter College of Education faculty will modify scenario scripts and develop new ones specifically focused on science and mathematics concepts in the context of high-poverty, urban schools with large ethnically diverse student populations. The project, in partnership with elementary and middle schools in the Los Angeles Unified School District in East Los Angeles and other smaller area districts, intends to investigate three research questions associated with the impact of mixed-reality virtual classroom simulations in K-8 teacher preparation. This project will modify and develop scripts using Mursion-TeachLivE software, based on work originally developed at the University of Central Florida. The project’s modified curriculum using mixed reality classroom simulation experiences integrated into several courses uses scenarios directed by faculty-developed scripts for teaching science and mathematics concepts to avatars that simulate elementary and middle school students. Through these simulations, pre-service teachers can present science and mathematics content, review content, practice behavior management skills, practice specific instructional techniques such as scaffolding, or perform many other daily tasks that a teacher would experience in an in-person classroom. The overarching intent of this project is to improve pre-service teachers’ ability to teach mathematics and science in urban schools. This goal will be achieved by studying the implementation by pre-service teachers of a virtual simulated classroom. The project’s research study intends to use participants from two cohorts of elementary pre-service teachers in a credentialing program. It aims to follow each cohort through three semesters in three courses and their student teaching, a fourth course. Data to be collected consists of: 1) course assignments, including science and mathematics lesson plans and a COVID classroom management plan, completed by pre-service teachers in experimental and control groups; 2) video recordings of the experimental group enrolled in courses using the virtual classroom simulations to practice lesson delivery and classroom management; 3) midterm and final assessments of pre-service teachers teaching by faculty supervisors; 4) focus groups of both the experimental and control group pre-service teachers; 5) virtual simulated classroom scripts prepared by the College faculty; and 6) a faculty focus group. Quantitative (statistical) and qualitative (content and discourse analyses) methods will be employed for data analysis. Project success has the potential to better prepare hundreds of future K-8 teachers to provide their elementary students with learning success in mathematics and science by meeting the challenges and opportunities they will find in ethnically diverse, high-poverty, urban classrooms. The dissemination of project findings, as well as sustaining and institutionalizing the project through continued collaboration with school partners has the potential for significant and measurable future broader impacts for the targeted K-8 students, for their families, and for their communities. 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.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": "607", "attributes": { "award_id": "2040186", "title": "NSF Student Participation Grant for 2020 IEEE International Conference on Green and Sustainable Computing (IEEE IGSC)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 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": "2020-09-01", "end_date": "2021-08-31", "award_amount": 10000, "principal_investigator": { "id": 1344, "first_name": "Mahdi", "last_name": "Nikdast", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "This proposal is for support of participation in the 2020 International Green and Sustainable Computing Conference (IGSC). Due to the COVID pandemic, this year's conference is virtual. There will be no travel but the award allows for expanded participation. IGSC has established itself as the premier conference in this area. Green computing is a newly evolving research area that is establishing itself as an interdisciplinary research area spanning across the fields of computer science and engineering, electrical engineering and other engineering disciplines. Green computing or sustainable computing is the study and practice of using computing resources efficiently, which in turn can impact a spectrum of economic, ecological, and social objectives. Such practices include the implementation of energy-efficient central processing units and peripherals as well as reduced resource consumption. In addition, IGSC covers applications and systems that use computing and information systems to accomplish sustainability in power grid, environment, climate, agriculture, and many other social and economic applications. The conference offers researchers, industry participants, and students the forum for a growing awareness of the study and practice of using computing resources efficiently, which in turn can impact a spectrum of economic, ecological, and social objectives.The grant encourages students interested in green or sustainable architecture, networks, circuit design, software, and applications, as well as students interested in use of computing for sustainability in other areas, to find useful information on the topics of their choice. It also provides an excellent opportunity for many students nationwide to get familiar with and engage in a new research area of national importance. Students not having a paper in IGSC or otherwise without the resources that allow the opportunity to attend IGSC will be eligible for these funds.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": "606", "attributes": { "award_id": "2001078", "title": "Collaborative Research: Using molecular functionalization to tune nanoscale interfacial energy and momentum transport", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1339, "first_name": "Nora", "last_name": "Savage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-03-31", "award_amount": 350801, "principal_investigator": { "id": 1342, "first_name": "Jarrod", "last_name": "Schiffbauer", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 322, "ror": "https://ror.org/0451s5g67", "name": "Colorado Mesa University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1340, "first_name": "Samuel E", "last_name": "Lohse", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1341, "first_name": "Christopher A", "last_name": "Dieni", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 322, "ror": "https://ror.org/0451s5g67", "name": "Colorado Mesa University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Engineering surfaces at the nanometer scale will play a crucial role in a wide range of future technologies, including water desalination/purification for drinking and agriculture, efficient heating/cooling, waste heat recovery, advanced energy generation and storage, as well as biomedical applications such as advanced diagnostics and therapeutics. The investigators seek demonstrate nanometer-scale and molecular-level tuning of material properties to create nano-engineered surfaces, or so-called “super-surfaces”. The project will also train diverse scientists and engineers through interdisciplinary science, technology, engineering, and math education. A pilot undergraduate nanoscience program will be created for undergraduates, including rural, first-generation, non-traditional, and Hispanic students. This will provide students, including underrepresented groups, an opportunity to research and network with faculty and students at a major research university. The goal of this project is to demonstrate a novel technique for molecular-level tuning of interfacial thermal conductance, surface charge, capillary properties, and biological interaction of solid-liquid interfaces using a model gold-alkanethiol-water system. By employing a highly synergistic, integrated experimental and theoretical approach (to design, synthesize, and then re-design microscale surfaces), the study will advance the fundamental understanding of mixed monolayer structure, dynamics, and interfacial interactions. These studies will extend to a systematic investigation of cooling rate and substrate curvature on functionalized thiol domain formation on both flat substrates and nanoparticles. By demonstrating a commercially scalable technique for tuning solid-liquid interfacial transport properties and biomolecular sensitivity of surfaces with nanometer precision, the project addresses significant applied research needs in the field. This work is anticipated to lead to the development of a new nanoscale manufacturing paradigm for the rational engineering of solid-fluid interfaces that can be applied to a broad range of functional molecules and substrates. Additionally, it will explore possible means to control interfacial transport and biological interactions with functionalized and nanostructured materials. Thus, these studies will provide considerable cross-cutting scientific and technological benefits, which will improve the overall quality of human life and health. Because the project will also establish a pilot collaborative nanoscience program including students from two primarily undergraduate institutions (Colorado Mesa University and Central Washington University), which serve large Hispanic, rural, first generation, and non-traditional student populations, with students and researchers at University of Notre Dame, this project will contribute to the diversity of the scientific workforce. Specifically, the integrated research and education design of the studies will aid in student engagement, retention, and success. Because of the COVID pandemic, the PIs at all three institutions are actively engaged in developing a plan for inter- and intra- institutional collaborative research during the pandemic, planning for increased laboratory safety and utilizing information technology solutions for communications to mitigate the disruptive effects of the pandemic on the project activities while assuring researcher safety. Lastly, through community outreach and education activities via Colorado Mesa University’s Eureka Science Museum and Maverick Innovation Center, the PIs will contribute to regional educational development and economic development through entrepreneurship.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": "605", "attributes": { "award_id": "2001079", "title": "Collaborative Research: Using molecular functionalization to tune nanoscale interfacial energy and momentum transport", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1337, "first_name": "Nora", "last_name": "Savage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-08-31", "award_amount": 73026, "principal_investigator": { "id": 1338, "first_name": "Tengfei", "last_name": "Luo", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Engineering surfaces at the nanometer scale will play a crucial role in a wide range of future technologies, including water desalination/purification for drinking and agriculture, efficient heating/cooling, waste heat recovery, advanced energy generation and storage, as well as biomedical applications such as advanced diagnostics and therapeutics. The investigators seek demonstrate nanometer-scale and molecular-level tuning of material properties to create nano-engineered surfaces, or so-called “super-surfaces”. The project will also train diverse scientists and engineers through interdisciplinary science, technology, engineering, and math education. A pilot undergraduate nanoscience program will be created for undergraduates, including rural, first-generation, non-traditional, and Hispanic students. This will provide students, including underrepresented groups, an opportunity to research and network with faculty and students at a major research university. The goal of this project is to demonstrate a novel technique for molecular-level tuning of interfacial thermal conductance, surface charge, capillary properties, and biological interaction of solid-liquid interfaces using a model gold-alkanethiol-water system. By employing a highly synergistic, integrated experimental and theoretical approach (to design, synthesize, and then re-design microscale surfaces), the study will advance the fundamental understanding of mixed monolayer structure, dynamics, and interfacial interactions. These studies will extend to a systematic investigation of cooling rate and substrate curvature on functionalized thiol domain formation on both flat substrates and nanoparticles. By demonstrating a commercially scalable technique for tuning solid-liquid interfacial transport properties and biomolecular sensitivity of surfaces with nanometer precision, the project addresses significant applied research needs in the field. This work is anticipated to lead to the development of a new nanoscale manufacturing paradigm for the rational engineering of solid-fluid interfaces that can be applied to a broad range of functional molecules and substrates. Additionally, it will explore possible means to control interfacial transport and biological interactions with functionalized and nanostructured materials. Thus, these studies will provide considerable cross-cutting scientific and technological benefits, which will improve the overall quality of human life and health. Because the project will also establish a pilot collaborative nanoscience program including students from two primarily undergraduate institutions (Colorado Mesa University and Central Washington University), which serve large Hispanic, rural, first generation, and non-traditional student populations, with students and researchers at University of Notre Dame, this project will contribute to the diversity of the scientific workforce. Specifically, the integrated research and education design of the studies will aid in student engagement, retention, and success. Because of the COVID pandemic, the PIs at all three institutions are actively engaged in developing a plan for inter- and intra- institutional collaborative research during the pandemic, planning for increased laboratory safety and utilizing information technology solutions for communications to mitigate the disruptive effects of the pandemic on the project activities while assuring researcher safety. Lastly, through community outreach and education activities via Colorado Mesa University’s Eureka Science Museum and Maverick Innovation Center, the PIs will contribute to regional educational development and economic development through entrepreneurship.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": "604", "attributes": { "award_id": "2021909", "title": "BII-Design: Exploring the ecology and evolution of the global virome with big data and machine learning", "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": 1334, "first_name": "Samuel", "last_name": "Scheiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2022-12-31", "award_amount": 166189, "principal_investigator": { "id": 1336, "first_name": "Colin J", "last_name": "Carlson", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1335, "first_name": "Tad", "last_name": "Dallas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "This Design activity will result in a proposal to create a Biology Integration Institute that will synthesize recent advances in wildlife virology and pursue new insights about the ecology and evolution of the global virome. The pandemic emergence of SARS-CoV-2 is only the latest development in an accelerating trend of dangerous viruses emerging from wildlife. Global travel, urbanization, and increasing human-wildlife contact have all made it easier for these viruses to emerge. In the future, climate change and land use change will reassemble the global virome even further, forcing mammals to cross continents, meet in new ecosystems, and exchange viruses thousands of times more, potentially unleashing even more threats to global health. At least 10,000 of these mammal viruses might have the potential to infect humans, but most of the global virome is still undescribed: only about 1% of mammal viruses have been discovered, and a much smaller fraction in other vertebrates. With so little data, it is difficult to predict which viruses will pose a future threat, or where, when, and how they could emerge. Predicting the next pandemic threat will require new data spanning biological scales, from single genes up to deep evolutionary time, and new statistical methods from the cutting edge of computer science and mathematics. In addition, the project will host summer residencies for trainees and develope new coursework that combines biology with hands-on computer science labs.The project assembles a group of virologists, computer scientists, statisticians, and ecologists to explore cutting edge scientific questions about methodology, inference, and impact. The project has three aims: (1) synthesizing existing data about host-virus associations for all vertebrate clades; (2) developing novel approaches to predict host-virus interaction networks, using novel data streams like viral strain diversity characterized from genomes, or receptor data from immunological studies; and (3) developing frameworks for actionable science that will put viral ecology to use for global health science and security. These aims will be accomplished through collaborative workshops. In doing so it will establish a foundation for a full Implementation proposal to develop an Emerging Virus Institute.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": "603", "attributes": { "award_id": "2024226", "title": "I-Corps: New image processing programs and data modeling algorithms for education environments", "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": 1332, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2021-12-31", "award_amount": 50000, "principal_investigator": { "id": 1333, "first_name": "Amir K", "last_name": "Miri", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 321, "ror": "https://ror.org/049v69k10", "name": "Rowan University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 321, "ror": "https://ror.org/049v69k10", "name": "Rowan University", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of an AI (artificial intelligence) solution that is aimed at enhancing student active learning. This technology platform is aimed at providing dynamic assessments of student performance throughout the academic year. The AI technology triggers timely interventions by providing early detection of struggling students as well as students with special talents. Unlike traditional platforms, this solution uses a combination of factors such as students’ behavior in the classroom, homework grades ,and regular test scores to evaluate risk levels and recommends generalized and personalized feedback plus identified routines for improving student learning performance. The platform also will communicate students’ progress to students/parents/teachers regularly. The proposed technology may enhance the learning experience by taking an approach that excludes the flaws of current system surfaced by the COVID-19 pandemic.This I-Corps project is based on the development of an AI (artificial intelligence) solution that uses advanced analytics to enhance students' learning performance. The proposed technology uses a a combination of AI tools such as computer vision, deep learning, machine learning, and natural language processing to thoroughly analyze students’ behavior inside and outside the classroom. It provides important prescriptive analytics and uses recommendation systems and collaborative filtering to provide dynamic feedback and identifies successful routines for improving the student learning performance. This project is based on several behavior data science studies and the power of advanced analytics for generating data-driven insights in education.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": "602", "attributes": { "award_id": "2033521", "title": "A1: KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies", "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": 1326, "first_name": "Lara", "last_name": "Campbell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-08-31", "award_amount": 4998900, "principal_investigator": { "id": 1331, "first_name": "Krzysztof W", "last_name": "Janowicz", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1327, "first_name": "Dean A", "last_name": "Rehberger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1328, "first_name": "Pascal", "last_name": "Hitzler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1329, "first_name": "Wenwen", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1330, "first_name": "Mark P", "last_name": "Schildhauer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 320, "ror": "", "name": "University of California-Santa Barbara", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.The goal of this project is to improve data-driven decision making and data analytics, specifically data analytics that involve geographic data. This project will create the “KnowWhereGraph” – a knowledge graph tool that specifically enables other data-analysis knowledge tools that have a geospatial component. GeoEnrichment describes the process by which data becomes augmented with a wide range of auxiliary information tailored to a geospatial study area (such as demographic data). GeoEnrichment tools significantly reduce the costs involved in acquiring, entering, and cleaning geo-data. Unfortunately, currently available geoenrichment services provide access to only pre-defined categories of information, do not effectively handle interconnected data, offer limited support for data integration, and are generally expensive. This project plans to make data-driven decision making and data analytics substantially more effective, accessible, and affordable. The project will merge novel Artificial Intelligence-based geoenrichment technologies with a knowledge graph that brings together open, cross-domain, densely integrated data spanning the human-environment interface. This project’s work is enabled by an open, freely usable knowledge graph. These graphs are a combination of scalable, Web-standard technologies, specifications, and data cultures for representing densely interconnected statements derived from structured or unstructured data across domains, in both human and machine-readable ways. The technology tools are designed to be useful to and useable by researchers, analysts, decision-makers, and the interested public in any domain or cross-domain activity requiring geospatial intelligence. This project includes strong partnerships with non-academic and academic stakeholders including 4 for-profit organizations, 2 government agencies, and one non-profit, as well as five academic partnerships: ESRI (Geographic Information Systems); Oliver Wyman, (commodity markets and supply chains), Princeton Climate Analytics (weather and climate information services), In10T (digital agriculture, farm partnerships); US Geological Survey (USGS), Natural Resources Conservation Service within the U.S. Department of Agriculture (USDA): and DirectRelief (humanitarian aid); as well as University of California Santa Barbara(UCSB), Kansas State University (K-State), Michigan State University (MSU), Arizona State University (ASU), and University of Southern California(USC). Additional partnerships are expected to develop during this Phase II effort. The “KnowWhereGraph” will be a valuable element of the Convergence Accelerator Phase II cohort, providing geospatial tools to the other projects within the cohort. In addition the project plans to focus on several strategic application areas that are likely to benefit US society, including: COVID-19 related supply chain disruptions and the US food, agriculture, and energy sectors, and their attendant supply chains generally; environmental policy issues relative to interactions among agricultural sustainability, soil conservation practice, and farm labor; and delivery of emergency humanitarian aid, within the US and internationally. Anytime knowing “where” is key, this project’s tools may be helpful. Formally, a knowledge graph consists of a massive set of statements, constructed from inter-connected node- and edge-labeled resources, allowing multiple, heterogeneous edges for the same nodes. A collection of definitional statements specifying the meaning of the knowledge graph's vocabulary is called its (KG) schema or ontology. The ontology is critical for rigorous logical interpretation and machine-actionability. Several innovations in knowledge graph technology will drive the project: (I) creating an open, web-accessible knowledge graph, with attendant methods and tools, to enable contributions to the graph from a range of sources; (II) developing strategies for semantically lifting imagery data, such as remotely sensed imagery and drone imagery, into this graph, thereby integrating vast amounts of data; (III) developing novel spatially-explicit AI-based methods, models, and services to enable geoenrichment on top of this graph; and (IV) developing both programmatic (application program interface, API) and human-accessible interfaces for the KnowWhereGraph. By merging the flexibility, expressive power, and community-driven features of open graph technologies with multi-format geospatial data and advanced geospatial intelligence, the KnowWhereGraph is designed to become a rich, integrative information resource that can transform and converge discovery, analysis, and synthesis within and across a multitude of fields and sectors.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": 1391, "pages": 1419, "count": 14184 } } }