Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&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=1397&sort=-awardee_organization", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=-awardee_organization", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-awardee_organization" }, "data": [ { "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 } }, { "type": "Grant", "id": "14424", "attributes": { "award_id": "2121656", "title": "MCA: Electronic Monitoring of Fisheries to Protect the Environment & Human Rights", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Evolutionary Processes" ], "program_reference_codes": [], "program_officials": [ { "id": 27031, "first_name": "Christine", "last_name": "Leuenberger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": null, "award_amount": 346983, "principal_investigator": { "id": 31045, "first_name": "Lekelia", "last_name": "Jenkins", "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": "Electronic monitoring (EM) has the potential to address environmental concerns around fishing, such as illegal, unreported, and unregulated fishing (IUU fishing). Electronic monitoring is a system of onboard cameras that records fishing catches for later analysis, verification, and alignment with fisheries regulations. There is a link between environmentally harmful fishing practices and the exploitation of people, with dire consequences for workers at sea (e.g. working conditions, occupational health and safety, forced labor, and modern slavery). Recent research indicated that the environmental data captured by EM may also reveal the social and labor conditions borne by workers and human observers on vessels. Some environmental groups and governments have articulated a vision of the potential of EM to save fish populations, while making the fishing industries’ work easier for data reporting, traceability, and subsequent profit increase. However, this vision pits human rights to decent working conditions and fair treatment against human rights to privacy.<br/><br/>This project is an exploratory study to investigate the overarching research question: How is the global environmental imaginary and competing sociotechnical imaginary shaping the current use and futures of EM in fisheries? The methods of investigation are semi-structured interviews and documents analyzed with a grounded theory approach, participant observation, foresight methods, and qualitative systems mapping (QSM). Constrained Choice Theory serves as a lens to identify emerging competing imaginaries and the drivers behind them. This project will: 1) contribute to the comparatively small literature of how institutions create imaginaries; 2) explore imaginaries at different scales from the global to local; 3) explore the drivers that lead to reconfigured imaginaries; 4) explore the socio-cultural and human dimensions surrounding EM; 5) and further develop the nascent method QSM. Importantly, the research project will advance both theoretical and applied understanding that could help reduce human rights abuses, including modern slavery, in fisheries.<br/><br/>This project is jointly funded by the Science and Technology Studies program in SBE and Division Of Environmental Biology in Biological Sciences.<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": "15095", "attributes": { "award_id": "2416742", "title": "Investigating the Uptake of Research-Based Instructional Strategies: A Post-COVID Update", "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": 27310, "first_name": "James A. M.", "last_name": "Alvarez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 113972, "principal_investigator": { "id": 31635, "first_name": "Naneh", "last_name": "Apkarian", "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 project aims to serve the national interest by mapping national patterns in the use of research-based instructional practices in post-secondary chemistry, mathematics, and physics courses five years after the disruptions due to the COVID-19 pandemic. In the Spring of 2019, a survey was sent to roughly 18,000 instructors of first-year mathematics, chemistry, and physics courses at nearly 1000 post-secondary institutions. That survey provided a comprehensive view of introductory science courses and instructors across the United States, with responses from nearly 4000 faculty from 660 U.S. colleges and universities. However, just one year later colleges and universities across the nation quickly shifted to online, emergency remote teaching in response to the COVID pandemic. The scale of instructional change during this time was both unprecedented and ubiquitous, with nearly every instructor teaching in the spring of 2020 required to try something new, and many needing to continue experimenting and revising their courses for the following semesters. This project will repeat the 2019 survey in order to characterize any lasting impact of the COVID pandemic on undergraduate science education and understand what this new instructional landscape may mean for change agents working to improve undergraduate science education through the uptake of research-based instructional practices.<br/><br/>The goals of this project are to 1) understand the impact of the COVID pandemic on undergraduate science education as well as provide a current description of undergraduate science instruction, and 2) in consideration of any shifts following the COVID disruption to higher education, revise and update the research-based insights and recommendations for supporting and achieving instructional change in undergraduate STEM. To do so, the roughly 18,000 instructors will be re-surveyed. Some of the survey analyses will be conducted on the new responses alone, including multilevel modeling of the impact of malleable factors on instructors’ adoption of research-based instructional practices. Other analyses will incorporate the prior results for pre-post analysis to capture changes in the practices of both individuals and the disciplines in the aggregate. Where changes are observed, additional statistical tests and modeling will be used to identify the impact of emergency response teaching strategies on those shifts. These findings will be used by change agents (e.g., professional development organizations, instructional coaches) to better support undergraduate instructors in implementing research-based instructional strategies and by administrators (e.g., department chairs, course coordinators) in making resource allocations and policy decisions. These results will update the foundational knowledge base needed to support widespread pedagogical shifts toward the use of research-based instructional practices in post-secondary STEM education, impacting undergraduate students across the country. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.<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": "15120", "attributes": { "award_id": "2421258", "title": "Collaborative Research: IHBEM: Beneath the Surface: Integrating Wastewater Surveillance and Human Behavior to Decode Epidemiological Patterns", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [ { "id": 622, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 99988, "principal_investigator": { "id": 31677, "first_name": "Yang", "last_name": "Kuang", "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": "How can disease outbreaks in an increasingly interconnected world be better predicted and responded to? The project tackles this challenge by combining two key sources of information: community wastewater and human behaviors. While current methods often rely on delayed and inaccurate medical reports, our innovative approach analyzes traces of viruses in sewage and incorporates various types of data about human activity. This includes information on people's movements, social interactions, online searches, social media posts, and immune factors. By combining these diverse data sources, the Investigators aim to detect diseases earlier and gain a more comprehensive understanding of how they spread through communities. The investigators will also examine how public attitudes and behaviors evolve during prolonged health crises. Although the initial focus is on COVID-19, the methods to be developed could be applied to other infectious diseases, helping communities worldwide prepare for future health emergencies. Beyond the research, the investigators are committed to training undergraduate and graduate students from diverse backgrounds, nurturing the next generation of public health professionals. Ultimately, this project will provide valuable tools for health officials to make quicker, more informed decisions to protect public health.<br/><br/>The goal of this project is to enhance mathematical epidemiological modeling by integrating human behavioral data with wastewater surveillance data, creating a more comprehensive and timely approach to outbreak detection and response. By synthesizing advancements across mathematical modeling, wastewater epidemiology, and geographic information science (GIScience), the research approach innovatively connects human behavior insights with wastewater data to enhance viral transmission understanding and forecasts at the community level. To achieve this, the Investigators will pursue three main objectives: (1) Develop an early-warning system using wastewater and digital and social behavior data; (2) Create a socio-immuno-epidemiological framework to examine the effectiveness of pharmaceutical interventions and the emergence of dominant variants using wastewater surveillance data; and (3) Model the impact of pandemic fatigue social behaviors on viral transmission at the community level. These objectives will be addressed by a interdisciplinary research team, which brings together expertise in applied mathematics, epidemiology, public health, and geography. This approach represents a significant step forward in understanding the complex interactions between human behavior, immune responses, and pathogen spread. Ultimately, the research outcomes will equip health officials with valuable tools for designing proactive, targeted, and adaptable interventions, enabling quicker and more informed decision-making. This award is co-funded by DMS (Division of Mathematical Sciences) and SBE/SES (Directorate of Social, Behavioral and Economic Sciences, Division of Social and Economic Sciences).<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": "378", "attributes": { "award_id": "2210137", "title": "EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Combatting Disinformation and Racial Bias: A Deep-Learning-Assisted Investigation of Temporal Dynamics of Disinformation", "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": 704, "first_name": "Daniela", "last_name": "Oliveira", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2024-05-31", "award_amount": 300000, "principal_investigator": { "id": 707, "first_name": "Kookjin", "last_name": "Lee", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 705, "first_name": "Kyounghee", "last_name": "Kwon", "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": 706, "first_name": "Doowon", "last_name": "Kim", "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 explores the diffusion of racial disinformation online and its social impacts, particularly focusing on Asian Americans. While the hatred and bias against Asian Americans have become notable amid the COVID-19 pandemic, Asian-targeting disinformation has yet been fully explored. The project's novelties are in unique multidisciplinary approaches to (1) detect Asian-targeting disinformation and its countermeasure messages, and understand how they are spread on the web, (2) examine how the spread of disinformation and countermeasure messages on the web is associated with the intensity of the bias and hate crimes against Asian Americans, and (3) develop various data-driven computational models to help understanding the disinformation dynamics. The project's broader significance and importance are to inform civil society, including advocacy organizations and the general public, about how to strategize communication efforts in battling racial disinformation, and to make the developed tools and outcomes publicly available for broader uses.The project takes three-staged approaches. The main objective of the first phase is to develop computational tools for the detection and analysis of the temporal dynamics between Asian-targeted disinformation and countermeasures on the Web. A specific focus is on developing automated identification tools and deep-learning classification models by feature-engineering unique characteristics of disinformation. The objective of the second phase is to understand to what extent the spread of disinformation and countermeasures online is associated with the societal trend of implicit bias and hate crime occurrences against Asian Americans in the real-world, which can be achieved via developing deep-learning causality models. The objective of the third phase is to design scalable data-driven deep-learning models of disinformation dynamics in macro and micro levels, identifying unknown dynamics from the real-world measurements, which also enables simulations of the learned dynamics.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": 1385, "pages": 1397, "count": 13961 } } }