Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1393&sort=-awardee_organization
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-awardee_organization", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-awardee_organization", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1394&sort=-awardee_organization", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-awardee_organization" }, "data": [ { "type": "Grant", "id": "13568", "attributes": { "award_id": "2144751", "title": "CAREER: Efficient, Dynamic, Robust, and On-Device Continual Deep Learning with Non-Volatile Memory based In-Memory Computing System", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software & Hardware Foundation" ], "program_reference_codes": [], "program_officials": [ { "id": 977, "first_name": "Sankar", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": null, "award_amount": 500000, "principal_investigator": { "id": 8714, "first_name": "Deliang", "last_name": "Fan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 173, "ror": "", "name": "The University of Central Florida Board of Trustees", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>Over past decades, there have existed grand challenges in developing high performance and energy-efficient computing solutions for big-data processing. Meanwhile, owing to the boom in artificial intelligence (AI), especially Deep Neural Networks (DNNs), such big-data processing requires efficient, intelligent, fast, dynamic, robust, and on-device adaptive cognitive computing. However, those requirements are not sufficiently satisfied by existing computing solutions due to the well-known power wall in silicon-based semiconductor devices, the memory wall in traditional Von-Neuman computing architectures, and computation-/memory-intensive DNN computing algorithms. This project aims to foster a systematic breakthrough in developing AI-in-Memory computing systems, through collaboratively developing ahybrid in-memory computing (IMC) hardware platform integrating the benefits of emerging non-volatile resistive memory (RRAM) and Static Random Access Memory (SRAM) technologies, as well as incorporating IMC-aware deep-learning algorithm innovations. The overarching goal of this project is to design, implement, and experimentally validate a new hybrid in-memory computing system that is collaboratively optimized for energy efficiency, inference accuracy, spatiotemporal dynamics, robustness, and on-device learning, which will greatly advance AI-based big-data processing fields such as computer vision, autonomous driving, robotics, etc. The research will also be extended into an educational platform, providing a user-friendly learning framework, and will serve the educational objectives for K-12 students, undergraduate, graduate, and under-represented students.<br/><br/>This project will advance knowledge and produce scientific principles and tools for a new paradigm of AI-in-Memory computing featuring significant improvements in energy efficiency, speed, dynamics, robustness, and on-device learning capability. This cross-layer project spans from device, circuit, and architecture to DNN algorithm exploration. First, a hybrid RRAM-SRAM based in-memory computing chip will be designed, optimized, and fabricated. Second, based on this new computing platform, the on-device spatiotemporal dynamic neural network structure will be developed to provide an enhanced run-time computing profile (latency, resource allocation, working load, power budget, etc.), as well as improve the robustness of the system against hardware intrinsic and adversarial noise injection. Then, efficient on-device learning methodologies with the developed computing platform will be investigated. In the last thrust, an end-to-end DNN training, optimization, mapping, and evaluation CAD tool will be developed that integrates the developed hardware platform and algorithm innovations, for optimizing the software and hardware co-designs to achieve the user-defined multi-objectives in latency, energy efficiency, dynamics, accuracy, robustness, on-device adaption, etc.<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": "13693", "attributes": { "award_id": "2121225", "title": "Bringing Authentic Research to the Remote Classroom: A Fully Online Course-based Undergraduate Research Experience for Astronomy Majors", "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": 3736, "first_name": "R. Corby", "last_name": "Hovis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": null, "award_amount": 299840, "principal_investigator": { "id": 15002, "first_name": "Ariel", "last_name": "Anbar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 6492, "first_name": "Ariel D", "last_name": "Anbar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 27675, "first_name": "Chris J", "last_name": "Mead", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by developing a scalable model for bringing research into online undergraduate programs. There has been a tremendous shift towards online learning in the past two decades. At the core of this initiative is the desire to make participation in research accessible to a diverse population of learners who may not be able to accommodate the standard model of full-time education on a physical campus. Participation in research is a critical component of any STEM degree, as it involves learners in the process of scientific inquiry, while improving students’ data literacy and critical thinking skills. Although participation in research is a common component of in-person STEM degrees, fewer opportunities exist for online students. This project will broaden student participation in research through the implementation of a course-based undergraduate research experience (CURE) geared towards students enrolled in online astronomy degree programs. This CURE will take the form of an upper-level research course that focuses on computational literacy and data replication. The emphasis on renewed analysis of existing data is an innovative and significant divergence from the way CUREs are typically conducted. Since multiple students can engage in analysis of the same dataset, this design will highlight the under-discussed role of result replication. As a result, this scalable replication-based model will be transferable to other institutions while simultaneously laying the groundwork for developing online CUREs for students across a variety of scientific disciplines. <br/><br/>The goal of this project is to address a common disparity between online and in-person STEM degree programs by providing students in the Astronomical and Planetary Sciences program with an upper-level research course that focuses predominantly on research literacy and data replication. This research course will emphasize the importance of repeated analysis as it pertains to exoplanet observational characteristics. Future space-based observations of exoplanets require ongoing maintenance of their predicted celestial position, also known as ephemerides. Replication-driven research of this kind will provide updated ephemerides, making meaningful contributions to current exoplanet research. Using established measures from prior work analyzing undergraduate research experiences, the project team will study the effectiveness of this course to determine the impact of an online CURE designed around the use of data analysis and reproducibility on student learning, research literacy, and student self-efficacy. The goal of this research is to advance understanding of the design components that are required in an effective CURE, knowledge that will inform the development of both online and in-person undergraduate research courses in the future. 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": "13704", "attributes": { "award_id": "2126303", "title": "CC* Compute: The Arizona Federated Open Research Computing Enclave (AFORCE), an Advanced Computing Platform for Science, Engineering, and Health", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Campus Cyberinfrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 2638, "first_name": "Kevin", "last_name": "Thompson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": null, "award_amount": 399997, "principal_investigator": { "id": 29959, "first_name": "Marisa", "last_name": "Brazil", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 29959, "first_name": "Marisa", "last_name": "Brazil", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 29960, "first_name": "Gil", "last_name": "Speyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 29961, "first_name": "Suren", "last_name": "Jayasuriya", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 29962, "first_name": "Susanne P", "last_name": "Pfeifer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>Drawing upon its mission to enable access to discovery and scholarship in science, engineering, and health, Arizona State University (ASU) is deploying the Arizona Federated Open Research Computing Enclave (AFORCE). AFORCE provides cutting-edge technology to support research and education while advancing the knowledge and understanding of deploying 21st-century cyberinfrastructure in a large public research university. Specifically, this state-of-the-art system is supporting multidisciplinary research and education in science, technology, engineering, and mathematics domains including computational genomics, molecular dynamics, computational materials science, robotics, and imaging.<br/><br/>To increase computational capacity, AFORCE comprises a pool of multiple graphical processing unit (GPU) accelerated computing nodes accessible to extramural researchers through federated authentication provided via InCommon. Moreover, the AFORCE system itself is part of the global Open Science Grid computing pool. ASU also promotes and enables the use of Open Science Grid by incorporating its capabilities into regular training sessions and faculty engagement events. Finally, AFORCE is configured to also provide cloud burst capabilities allowing compute jobs to be scheduled on commercial clouds. Early career faculty will be specifically targeted for workshops and tutorials, helping encourage their participation in the AFORCE system.<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": "13984", "attributes": { "award_id": "2141594", "title": "EAGER: The Geoscience behind Resilient Infrastructure Careers: Unlocking the Intrinsic Value of Diverse Communities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "GOLD-GEO Opps LeadersDiversity" ], "program_reference_codes": [], "program_officials": [ { "id": 1400, "first_name": "Brandon", "last_name": "Jones", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 299085, "principal_investigator": { "id": 30441, "first_name": "Vernon", "last_name": "Morris", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This proposal outlines the Emerging Geoscience Scholars (EGS) model, a novel approach towards nurturing talent and welcoming identity. The PIs propose to pilot new approaches to the engagement of professional societies with people in the cities that host their annual meetings. The PIs propose to start modestly with 20-30 scholars from historically underrepresented communities, disconnected youth, veterans, and transitional (unemployed or underemployed) people per year investigating careers and sharing their findings as emerging professionals at a major geoscience conference, the Fall Meeting of the American Geophysical Union (AGU). By sharing their work in a professional science meeting and putting the weight of a major scientific society and large academic institution behind those investigations, ideas can grow into a practice that can ultimately transform the geoscience infrastructure and workforce as well as other scientific and professional associations. With the emphasis that the Biden Administration has put on infrastructure and conservation, partnering with AGU, and the many professions it covers, is an ideal starting point for this experimental program.<br/><br/>This program aims to increase the capacity for a resilient and sustainable built infrastructure and advance climate justice, by ensuring that the geoscience and the wider infrastructure workforce of the future adopts values and methods that center on and embrace the core values of equity, justice, and inclusiveness - from diversity comes strength. A sustainable future workforce for the geosciences must be composed of, and ultimately led by, people of color, Native Americans, and immigrants. It must start now with this rising generation. This proposed learning/training ecosystem is designed to be an open-source model or template that can be adopted by other professional associations and partnering educational institutions nationwide; it thus contributes to the common educational and research infrastructure for the development of a diverse and globally competitive US workforce. By addressing issues important to urban coastal communities under pressures from climate change, the effort has the potential to improve the wellbeing of people in society. By educating and engaging youth and lifelong learners in local scientific and social issues, it helps develop a more involved and informed public as well as broadening racial and ethnic diversity in the geosciences<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": "14033", "attributes": { "award_id": "2122901", "title": "PFI-RP: Partnership for Innovation - Avoiding Kidney Injuries with Evidence-Based Smart Technology", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "PFI-Partnrships for Innovation" ], "program_reference_codes": [], "program_officials": [ { "id": 11472, "first_name": "Jesus Soriano", "last_name": "Molla", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 550000, "principal_investigator": { "id": 30529, "first_name": "Shaopeng", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 9205, "first_name": "Teresa", "last_name": "Wu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, { "id": 30529, "first_name": "Shaopeng", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 30530, "first_name": "Leslie", "last_name": "Thomas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 30531, "first_name": "Mary Laura", "last_name": "Lind", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Partnerships for Innovation – Research Partnerships (PFI-RP) project is to develop a new technology that improves the ability of doctors to diagnose acute kidney injury in hospitalized patients. Acute kidney injury is a medical condition which begins without clinical symptoms or signs, and has been estimated to occur in up to 20% of hospitalized patients. Acute kidney injury results in significant harm to patients as well as greatly increased overall health care costs. Acute kidney injury is generally not recognized early in its course. Currently available methods used to diagnose acute kidney injury are imperfect. Recognition of acute kidney injury using presently available methods is delayed and only occurs after the development of significant organ dysfunction. The technology that will be developed in this PFI-RP project may detect acute kidney injury earlier so that treatment can be rendered.<br/><br/>This project will develop a new technology for continuous, real time measurement and real time analysis of urinary biomarkers of acute kidney injury. Specifically, a system for continuous measurement of urine components will be developed and built. This system will be used to measure and quantify urine components and develop a model of early acute kidney injury. A machine learning prediction model will be developed and tested with the model system.<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": "14051", "attributes": { "award_id": "2126291", "title": "CC* Integration-Large: (BLUE) Software-Defined CyberInfrastructure to enable data-driven smart campus applications", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Campus Cyberinfrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 2638, "first_name": "Kevin", "last_name": "Thompson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 500000, "principal_investigator": { "id": 30561, "first_name": "Jessica", "last_name": "Shoop", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30559, "first_name": "Baoxin", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 30560, "first_name": "Xuesong", "last_name": "Zhou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>A new campus infrastructure called BLUE is developed to enable efficient and secure data-driven research and application development based on distributed IoT devices. BLUE addresses three main challenges for supporting innovative smart campus applications based on distributed IoT devices: (a) establishing a programable campus infrastructure to support distributed and ad hoc IoT services, (b) providing strong security and privacy protection of IoT data, and (c) constructing an edge-cloud infrastructure to provide computing, networking, and storage resources to support smart-campus applications. <br/><br/>BLUE is a new software-defined infrastructure to support IoT-based data processing, analysis, and distribution over distributed IoT data sources. BLUE also supports a set of tangible metrics, such as network QoS metrics, location, resource consumption, etc., to effectively enable researchers to validate their research models. Moreover, BLUE takes privacy and security protection as a fundamental enabling technique by pushing the computation towards the edge computing and networking infrastructure. Research applications built on this project share a common requirement for low-latency transfer of ever-larger data sets with collaborators across multiple geographic sites. This project will contribute to a national paradigm of campus-level dynamic network services that enables leading-edge network and domain-specific research.<br/><br/>BLUE can benefit the full range of campus scholarly activities, including research activities funded by NSF and other federal agencies. The outcomes of this project will be shared with the public based on an open-source license agreement. In addition, undergraduate and graduate student researchers will receive diverse STEM skills training, including networking research, big data analysis, and domain-specific research.<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": "14121", "attributes": { "award_id": "2120943", "title": "MCA: Career Advancement in Polar Cyberinfrastructure: Permafrost Feature Mapping and Change Detection using Geospatial Artificial Intelligence and Remote Sensing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Polar Cyberinfrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 30667, "first_name": "Marc", "last_name": "Stieglitz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 359841, "principal_investigator": { "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": [] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>Polar regions play a vital role in Earth’s climate, ecosystems, and economy. Unfortunately, climate change is driving dramatic changes in the Arctic ecosystem, endangering its natural environment, infrastructure, and lives. Arctic permafrost, ground that remains below 0°C for at least two consecutive summers, is at the center of this change. Covering nearly 1/4 of the land in the northern hemisphere, thawing permafrost is causing a significant local and regional impact in the Arctic. Severe impacts include land subsidence resulting in costly damage to the built environment and increased release of greenhouse gases which further exaggerates the greenhouse effect and global warming. To improve our understanding of permafrost dynamics and its linkages to other Arctic ecosystem components in the midst of rapid Arctic change, it is critically important to have geospatial data readily available that provide high-resolution mapping of permafrost features, their geographical extent, distribution, and change. Although a coarse classification of pan-Arctic permafrost has been developed, fine granularity, local to regional-scale mapping of major permafrost features, is largely unavailable. This data gap inevitably constrains us from gaining a holistic view of the space-time dynamics of permafrost degradation across the Arctic. The goal of this project is to bridge this existing data gap by developing new analytical solutions to support intelligent and automated delineation of permafrost features at scale.<br/><br/>Through a partnership with colleagues at Woodwell Climate Research Center, this project will explore novel ways to deepen the integration of cutting-edge AI, geospatial analysis, and cyberinfrastructure into Arctic permafrost research. Specifically, novel GeoAI (Geospatial Artificial Intelligence) solutions will be developed to empower the ongoing efforts of AI-based, high-resolution mapping of pan-Arctic permafrost thaw from Big Imagery. By enabling location-aware and multi-source deep learning and the integration of key spatial principles (i.e., spatial dependency and spatial autocorrelation), the proposed GeoAI model will create polar data products with high veracity and automation, thereby accelerating the scientific navigation of the New Arctic. A joint initiative, “Women in Polar Cyberinfrastructure,” will broaden the participation of women and underrepresented minorities in Arctic AI research. It will also serve as an important avenue for openly sharing knowledge and resources and provide mentorship to early-career scholars in Arctic science, GeoAI, and cyberinfrastructure. All datasets and tools produced in this project will be open-sourced and made available in the NSF Permafrost Discovery Gateway to increase their reuse and inspire further innovation.<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": "14130", "attributes": { "award_id": "2127348", "title": "NNA Research: Collaborative Research: Frozen Commons: Change, Resilience and Sustainability in the Arctic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "NNA-Navigating the New Arctic" ], "program_reference_codes": [], "program_officials": [ { "id": 15971, "first_name": "Roberto", "last_name": "Delgado", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 424999, "principal_investigator": { "id": 30677, "first_name": "John", "last_name": "Anderies", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30676, "first_name": "John M", "last_name": "Anderies", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>Navigating the New Arctic (NNA) is one of NSF's 10 Big Ideas. NNA projects address convergence scientific challenges in the rapidly changing Arctic. This Arctic research is needed to inform the economy, security and resilience of the Nation, the larger region and the globe. NNA empowers new research partnerships from local to international scales, diversifies the next generation of Arctic researchers, enhances efforts in formal and informal education, and integrates the co-production of knowledge where appropriate. This award fulfills part of that aim by addressing interactions among social systems, the natural environment, and the built environment in the following NNA focus areas: Arctic Residents, Data and Observation, Education, Forecasting, and Resilient Infrastructure. <br/><br/>This project applies convergent methodologies to study the Arctic Frozen Commons (FC), defined as the ice, snow, and permafrost landscapes collectively used and governed by communities and numerous non-local stakeholders. While significant knowledge exists around biophysical characteristics of the cryosphere, this remains largely separate from its cultural and social understandings among local and Indigenous communities, culminating in poor integration around the use and governance of Frozen Commons in a rapidly changing Arctic. An enhanced understanding of interacting processes in the social, cultural, technological, environmental, and governance domains for frozen commons is critical to framing sustainable Arctic futures. This project advances transdisciplinary research by converging Arts, Science, Local and Indigenous Knowledge systems (ArtSLInK) for developing a deeper understanding of FC resilience and sustainability. ArtSLInK encompasses synchronous, equitable, co-productive engagement across the social and natural sciences, the arts and place-based local and Indigenous knowledge systems, each with their distinct modes of exploration and expression.<br/><br/>This project integrates social, technological, and environmental domains of frozen commons, and explicitly engages with governance implications across diverse worldviews and management narratives. The project addresses three research questions: 1) What FC are recognized by culturally diverse Indigenous and local communities and regional stakeholders, 2) How are they governed within specific cultural domains?, and 3) What are the major sociocultural, environmental, technological, and infrastructural driving forces and their interrelations that affect the resilience and sustainability of FC? The project pursues the following objectives: (1) to identify and inventory community relevant FC; (2) to situate knowledge of FC using a social-ecological-technological systems (SETS) framework; (3) to understand interactions between sociocultural, environmental, and technology infrastructure domains affecting the availability, quality, and use of FC; and (4) to use integrated modeling approaches to determine sources of resilience and sustainability for FC under changing conditions. The project applies a transdisciplinary and comparative research framework for two rural-urban community pairs in Russia and the U.S. (Alaska) that are representative of different community sizes, governance regimes, socioeconomic arrangements, and geographies.<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": "14176", "attributes": { "award_id": "2128747", "title": "Doctoral Dissertation Improvement Award: A Microevolutionary Analysis of Population change", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Archaeology DDRI" ], "program_reference_codes": [], "program_officials": [ { "id": 7884, "first_name": "John", "last_name": "Yellen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 25191, "principal_investigator": { "id": 30745, "first_name": "Tisa", "last_name": "Loewen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30744, "first_name": "Tisa N", "last_name": "Loewen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>This research uses data from archaeological cemeteries to study the interactions among borderland peoples engaged in power struggles for territory and how impacts by conquerors influence regional population relationships. Previous research has shown that cultural groups respond differently to expansion into their homeland. What is not yet clear is whether biological relatedness correlates with the sharing of cultures and lifestyles that result from these interactions. Specifically, how prominent was the change in regional gene pools as societies became more culturally intertwined? Nevertheless, it is well established that colonized people adopt richly diverse identities despite expectations to culturally integrate. When common regional histories are known, identifying groups with shared genetic relationships provides a key component towards understanding their social identity construction. Closed and unchanging societies have not proven to be the norm in the past, and exploring these concepts archaeologically is important because it questions typological and static ideas about culture. This research provides a reading of the past that finds traction in modern discussions of social identities and self-determination. The project provides open-source data to be shared with other scholars for additional comparative studies. It generates a novel bioarchaeological model for understanding regional relationships and enhances educational and training opportunities for students and mentees. <br/><br/>This research uses changes in heritable dental phenotypes as indicators of gene flow, a methodological approach which can be applied in other contexts, with existing data sets, and is non-destructive. Assessment of shared genetic histories is used to determine if contacted populations were subject to biological assimilation. Recognition of unanticipated ancestral ties can be an indication of crafting of identities that flourished in complex ways and encourages archaeological studies to reconsider typological classifications. Only after resolving what is unknown, “how were people related?”, can what is known historically be appropriately applied.<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": "14241", "attributes": { "award_id": "2122621", "title": "Doctoral Dissertation Research: Unveiling Conceptual Shifts and Novel Dynamics in Genetic Engineering Science: A Gene Drive Case Study", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Science of Science" ], "program_reference_codes": [], "program_officials": [ { "id": 3635, "first_name": "Georgia", "last_name": "Kosmopoulou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-15", "end_date": null, "award_amount": 15129, "principal_investigator": { "id": 30809, "first_name": "Cody", "last_name": "OToole", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30809, "first_name": "Cody", "last_name": "OToole", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "How is knowledge created at the intersections between basic science, biotechnology, and industry? Gene drives are an interesting example, as they combine a long-standing interest with a recent technological breakthrough and a new set of commercial applications. Gene drives are genes engineered such that they are preferentially inherited at a frequency greater than the typical Mendelian fifty percent. During the historical and conceptual evolution of gene drives beginning in the 1960s, there has been many innovations and publications. Along with that, gene drive science developed considerable public attention, explosion of new scientists, and variation in the way the topic is discussed. It is now time to look at this new organization of science using a systematic approach to characterize the system which has enabled knowledge to grow in this scientific field. This project will break new ground in how knowledge advances in genetic engineering science, and how we understand what a “gene drive” is through analysis of language, communities, and social media. In effect, this research will advance multiple fields and enable a deeper understanding of knowledge and complex systems by a wide audience through publicly available dissemination of results through conferences, blogs, GitHub, and scholarly publications. <br/><br/>This project will document patterns of publication, collaborative relationships, social media influence, then combine those factors to characterize the knowledge system into a signal detection algorithm to predict the future trajectory of the larger CRISPR-Cas9 science. The results of computational analysis will provide an in-depth and complete characterization of the structure, dynamics, and evolution of scientific knowledge found in the gene drive technology. In addition, the project will analyze how the public opinion influences the progress of genetic engineering technologies through social media and news platforms. Further, time series analysis of the multiple layers of discourse will enable a diachronic connective mapping of collaborative relationships and track linguistic variation and change, highlighting where ambiguous language may appear. Thus, improving and creating more cohesive scientific language. Overall, depicting the structure, dynamics, and evolution of scientific knowledge during a novel eruption of scientific complexity can shed light on the factors that can lead to: (1) improved scientific communication, (2) reduction of scientific progress, (3) new knowledge, and (4) novel collaborative relationships. Therefore, characterizing the current technological, methodological, and social contexts that can influence scientific knowledge. Research results will reach conferences, blogs, GitHub, and be shared through both traditional and digital publications. Scholarly results will be available through different websites, and as much as possible will be shared for free.<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 } } ], "meta": { "pagination": { "page": 1393, "pages": 1405, "count": 14046 } } }