Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=2&sort=keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=keywords" }, "data": [ { "type": "Grant", "id": "2048", "attributes": { "award_id": "2029292", "title": "RAPID: Collaborative Research: Relationships, social distancing, social media and the spread of COVID-19", "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": [ "025Z", "065Z", "096Z", "7434", "7914" ], "program_officials": [ { "id": 5498, "first_name": "Jan", "last_name": "Leighley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 117340, "principal_investigator": { "id": 5499, "first_name": "Katherine", "last_name": "Ognyanova", "orcid": "https://orcid.org/0000-0003-3038-7077", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://covidstates.org/']", "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "This project seeks to improve the national response to the COVID-19 pandemic by launching large-scale data collection through a rolling national survey linked to individual social media data. We generate information useful to policymakers and local authorities and offer near-real-time state-by-state disease tracking. Our data allow officials to understand where the virus is currently spreading, facilitating improved allocation of resources. We also evaluate the networked nature of the disease, tracking its flow based on the reported social relationships of the survey participants and their social distancing behaviors. The project captures how well the information and communication needs of Americans are met during this crisis, observes patterns of citizen compliance with government recommendations, stay-at-home orders, and enforced lockdowns, and assesses their impact on suppressing the spread of the virus among diverse populations.The project has two core objectives: (1) producing information that will be immediately useful in improving the national response to COVID-19; and (2) using COVID-19 data to understand how people adapt to and make sense of a national crisis that has important and immediate ramifications for their daily lives. We rely on a large-scale, rolling national survey that is conducted on a daily basis, with approximately 3000 respondents per day. We also link the survey data to the social media behavior of respondents. The large sample sizes collected daily offers near-real-time state-by-state disease tracking, as well as the ability to observe key differences in responses to policies across demographic groups. The design will capture how people use technology to work, get informed, and stay connected, and respondents’ financial difficulties, employment experiences, and parenting and educational challenges in response to the pandemic.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": "1024", "attributes": { "award_id": "2136142", "title": "S2I2: Impl: The Molecular Sciences Software Institute", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 2505, "first_name": "Richard", "last_name": "Dawes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2026-07-31", "award_amount": 3825190, "principal_investigator": { "id": 2510, "first_name": "Thomas D", "last_name": "Crawford", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 2506, "first_name": "Shantenu", "last_name": "Jha", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2507, "first_name": "Teresa L", "last_name": "Head-Gordon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2508, "first_name": "Theresa L", "last_name": "Windus", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2509, "first_name": "Dominika", "last_name": "Zgid", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Molecular Sciences Software Institute (MolSSI) is supported by a joint award from the Divisions of Chemistry (CHE) and Molecular and Cellular Biosciences (MCB) and the Office of Advanced Cyberinfrastructure (OAC). Since its launch in 2016, the MolSSI has served as a nexus for the broad computational molecular sciences community by providing software expertise, community engagement and leadership, and education and training. Through a broad array of software infrastructure projects, teaching workshops, and community outreach, the MolSSI catalyzes the scientific advances needed to solve emerging scientific computing Grand Challenges. In its next phase, supported by this award, the MolSSI will capitalize on this success by continuing and extending its efforts for an even broader impact on our community's ability to address key scientific areas, including developing new energy technologies, fighting COVID-19 and future pandemics, developing climate solutions, exploring quantum information sciences, artificial intelligence and machine learning, and developing a more diverse and resilient workforce.Through the MolSSI's Software Scientists – a team of software engineering experts, drawn from the molecular sciences, computer science, and applied mathematics – the Institute will promote improved interoperability of community codes, easier deployment on heterogenous computing architectures, and greater parallel scalability of existing and emerging theoretical models. The MolSSI will help train the next generation of computational molecular scientists in modern software engineering tools and best practices through its Education Initiative that annually reaches thousands of students worldwide and its Software Fellowship program, which has already benefitted nearly 100 graduate students and postdoctoral fellows across the U.S. The MolSSI's Software Workshop program will bring the community together to identify and address the highest priority challenges and the MolSSI's Discovery, Outreach, and Sustainability programs will provide a key mechanism for community buy-in, provide insight into the needs of the molecular sciences community, and facilitate interactions among its members. The MolSSI's ultimate goal is to enable new science and broader impacts by building a community of molecular scientists prepared to provide solutions to problems impacting national health, social, environmental, and economic challenges.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": "3072", "attributes": { "award_id": "1934962", "title": "HDR TRIPODS: Collaborative Research: Foundations of Greater Data Science", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "HDR-Harnessing the Data Revolu" ], "program_reference_codes": [], "program_officials": [ { "id": 9528, "first_name": "Huixia", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-09-15", "end_date": "2022-08-31", "award_amount": 814165, "principal_investigator": { "id": 9534, "first_name": "Mujdat", "last_name": "Cetin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 464, "ror": "https://ror.org/022kthw22", "name": "University of Rochester", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 9529, "first_name": "Alex", "last_name": "Iosevich", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 9530, "first_name": "Daniel", "last_name": "Gildea", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 9531, "first_name": "Daniel", "last_name": "Stefankovic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 9532, "first_name": "Tong Tong", "last_name": "Wu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 464, "ror": "https://ror.org/022kthw22", "name": "University of Rochester", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The University of Rochester and Cornell University jointly establish the Greater Data Science Cooperative Institute (GDSC). The GDSC is based on two founding tenets. The first is that enduring advances in data science require combining techniques and viewpoints across electrical engineering, mathematics, statistics, and theoretical computer science. The investigators' goal is to forge a consensus perspective on data science that transcends any individual field. The second is that data-science research must be grounded in an application domain. This helps to ensure that assumptions about the availability and quality of data are realistic, and it allows methodological results to be tested experimentally as well as theoretically. As such, the GDSC aims to consider applications in medicine and healthcare, an important application domain and one for which advances in data science can have a direct, positive impact on society. The GDSC aims to tackle foundational questions that are motivated by problems in healthcare, obtain solutions that fuse domain expertise with application-agnostic methodologies, and ultimately yield scientific advances that impact the way healthcare is provided. The GDSC aims to leverage the physical proximity of the two institutions, and the unique strengths in each of the core disciplines above and in medicine.\n\nThe GDSC's cross-disciplinary research directions include: (i) Topological Data Analysis. The challenges that high-dimensional, incomplete, and noisy data present are great, but in many applications, exploiting the topological nature of the problem is possible. GDSC aims to develop new fundamental methods and theory to rigorously explore the promise of this unique approach. (ii) Data Representation. Data compression, embeddings, and dimension reduction play a fundamental role in data science. Inspired by new core challenges in biomedical imaging, genomics, and neural-spike training data, GDSC aims to develop novel source models and distortion measures, and ultimately seek a unifying theoretical framework across domains and disciplines. (iii) Network & Graph Learning. Many of the fundamental challenges in applying data science to non-homogeneous populations are best explored through a network or graph structure. GDSC aims to develop new techniques for parameter-dependent eigenvalue problems in spectral community detection, density-estimation methods on networks, and a theoretical framework for time-varying graphical models to study dynamic variable relations in time-evolving networks. (iv) Decisions, Control & Dynamic Learning. Sequential decisions are high-stakes in medicine. GDSC aims to utilize systems and control-engineering methods to improve health and disease management and develop new foundational theories and methods for label-efficient active learning and dynamic treatment regimes. (v) Diverse & Complex Modalities. Big data is complex data, and major new innovations are needed. GDSC aims to develop theoretical frameworks for inference under computational and privacy constraints and for high-dimensional data without parametric model assumptions. Text, image, and audio data present further challenges. To address such challenges, GDSC aims to explore transition systems for graph parsing of natural language and new fusion approaches for fully multimodal analysis. \n\nThis project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2816", "attributes": { "award_id": "1925596", "title": "CC* Compute: Accelerating Computational Research for Engineering and Science (ACRES) at Clarkson University, A Campus Cluster Proposal", "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": 8385, "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": "2019-07-01", "end_date": "2021-06-30", "award_amount": 396950, "principal_investigator": { "id": 8387, "first_name": "Joshua", "last_name": "Fiske", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 597, "ror": "https://ror.org/03rwgpn18", "name": "Clarkson University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 8386, "first_name": "Brian", "last_name": "Helenbrook", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 597, "ror": "https://ror.org/03rwgpn18", "name": "Clarkson University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Clarkson University is building a computational cluster (ACRES: Accelerating Computation Research for Engineering and Science) to support data and computationally intensive projects aligned with Clarkson's four interdisciplinary research themes: Data Analytics, Healthy World Solutions, Advanced Materials Development, and Next Generation Healthcare. ACRES facilitates the conduct of high-impact, collaborative research that requires access to high-performance computing (HPC) resources, enables research currently not practical/feasible, and also supports student-learning opportunities through credit-bearing courses, undergraduate research, and an existing NSF REU site focusing on HPC. As a campus resource, ACRES is made available to any faculty member or student at the University according to queueing policies implemented to ensure fair-access. And, ACRES supports Clarkson's increased focus on computational research and a cluster hire of computationally active faculty. \n\nThe ACRES compute cluster replaces an existing, five-year-old high-performance compute cluster whose computational capacity provided 1.05M core-h/yr. Research need for computational capacity has grown to an identified total of 8.5M core-h/yr. ACRES is sized to meet current demands and modest near-term growth with unused computational capacity being shared via the Open Science Grid (OSG) to benefit the broader scientific community. This new computational resource provides 9.8M core-h/year through 1120 cores, high-speed Infiniband interconnect, four NVIDIA Tesla V100 GPUs, and 40 TB of scratch storage.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "8448", "attributes": { "award_id": "1U01DD001293-01", "title": "Component A: North Carolina - Advancing Developmental Research using SEED and SEED Follow-up data", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2021-07-01", "end_date": "2026-06-30", "award_amount": 581307, "principal_investigator": { "id": 24212, "first_name": "Julie L", "last_name": "Daniels", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 817, "ror": "", "name": "UNIV OF NORTH CAROLINA CHAPEL HILL", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 817, "ror": "", "name": "UNIV OF NORTH CAROLINA CHAPEL HILL", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "- SEED Follow-up Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that impacts approximately 1.5% of children in the United States. Individuals with ASD experience deficits in social communication or restricted interests and repetitive behavior; but the severity and patterns vary greatly and convey lifelong impairment for some. It is unclear how the presentation of ASD changes from early childhood into adolescence or adulthood. The causes of ASD are also unknown, though substantial evidence supports the contribution of both genes and environmental factors. These gaps in knowledge exist because US studies to date have lacked the sample size, depth of data collection, or appropriate life course timing to address these questions. The Study to Explore Early Development (SEED) is now able to address these prior limitations. SEED is a large case- control study of children ages 2-5 years and their families, implemented across eight states over three phases. SEED collected detailed data on children’s core ASD symptoms, cognitive status, and presence of co- occurring conditions in early childhood, along with extensive risk factors related to maternal health and the perinatal environment as well as genomics. The SEED sample includes 2044 children with ASD, 1950 children with non-ASD developmental disabilities (DD), and 2285 population control children (POP), making this the largest etiologic study of ASD in the US. Recent ancillary studies - the SEED Teen Pilot and SEED COVID studies -- will soon add data on adolescent health and the consequences of the pandemic, respectively, for some SEED participants. The work proposed here, SEED Follow-up Studies (SEED FU), will maximize the impact of extant SEED data through analyses that characterize ASD phenotypes and assess the potential interplay between genetic and modifiable risk factors. SEED FU will also facilitate new data collection in middle childhood, adolescence and early adulthood to characterize changes in ASD phenotype across developmental stages, and the associated health, educational, and service needs across the early life course. These data will further enable prospective analyses of associations between early life factors and later childhood through early adulthood outcomes. Studying risk factors in relation to life course phenotypic subgroups may also help elucidate etiologies previously masked in ASD case-control studies. The NC SEED Team in combination with the SEED Network’s collaborative infrastructure and extensive extant data resources, will ensure the successful implementation of the SEED FU Study in North Carolina and contribute to success across the network. SEED is well-powered for making significant contributions to our understanding of the complex autism phenotype and identifying factors associated with ASD risk in the population. The knowledge gained by SEED FU will greatly advance our ability prevent adverse developmental outcomes and to support individuals with ASD and their families to ensure optimal wellbeing through early adulthood.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "3840", "attributes": { "award_id": "1707069", "title": "WERF: Determining the fate and major removal mechanisms of microplastics in water and resource recovery facilities", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EnvE-Environmental Engineering" ], "program_reference_codes": [], "program_officials": [ { "id": 12614, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2017-08-01", "end_date": "2021-07-31", "award_amount": 304892, "principal_investigator": { "id": 12616, "first_name": "Belinda", "last_name": "Sturm", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 415, "ror": "", "name": "University of Kansas Center for Research Inc", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 12615, "first_name": "Edward", "last_name": "Peltier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 415, "ror": "", "name": "University of Kansas Center for Research Inc", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "Proposal: 1707069\nPI: Belinda Sturm\n\nThe focus of this project is the fate of microplastics (plastics < 5mm) in the liquid and biosolids discharged from water resource and recovery facilities (WRRFs). Microplastics are typically entrained within activated sludge and ultimately released to the environment through biosolids. The detrimental effects of plastics on marine vertebrates is well-documented and a major environmental concern. In this project the transport pathways for plastics will be identified. The results of this study will help reduce harmful marine ecosystem impacts. The PIs will engage municipalities through a full-scale sampling campaign and will disseminate the data in a web-based database that is publically accessible. They will continue to collaborate with high school teachers to refine teaching modules dealing with topics focused on microplastics and emerging contaminants.\n\nMicroplastics are likely to be removed when they are adsorbed or entrained within the activated sludge floc structure. The main hypothesis is that the sludge structure and extracellular polymeric substances (EPS) content are controlling variables to microplastic removal. In particular, the assumption is that microbial aggregates with high surface areas and high EPS content can capture more microplastics. To test this hypothesis the PIs will conduct a survey of select WRRFs with different primary and secondary treatment processes. To further quantify microplastics capture efficiencies, the PIs will determine the effect of EPS on microplastic adsorption and retention efficiency within lab-scale and pilot-scale reactors and compare conventional and aerobic granular sludge processes for microplastic adsorption. The activated sludge process, and particularly gravity sedimentation, was not designed to remove low density microplastic particles. Microplastics are likely to be removed when they are adsorbed or entrained within the activated sludge floc structure. As microplastic loads to WRRFs increase, it is important to study the effect of niche separation of microplastic-associated microorganisms on activated sludge process performance. One outcome of the research will be a better understanding the fate of microplastics in WRRFs. Results of this project will provide a framework for comprehensive management of microplastics contamination in WRRFs.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "4608", "attributes": { "award_id": "1439327", "title": "CISE/CCF: 2014 Summer School on Formal Techniques", "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": 15901, "first_name": "Nina", "last_name": "Amla", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2014-04-01", "end_date": "2015-03-31", "award_amount": 85000, "principal_investigator": { "id": 15902, "first_name": "Natarajan", "last_name": "Shankar", "orcid": null, "emails": "", "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": [], "awardee_organization": { "id": 325, "ror": "https://ror.org/05s570m15", "name": "SRI International", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Formal verification techniques such as model checking, satisfiability solving, theorem proving, and static analysis have matured rapidly in recent years. These techniques have a number of important applications ranging from the modeling and analysis of biological and cyber-physical systems to the verification of the safety and security of complex software systems. The Summer School on Formal Techniques trains students in the principles and practice of formal verification, with a strong emphasis on the hands-on use and development of this technology. It primarily targets graduate students and young researchers who are interested in using verification technology in their own research in fields such as computing, engineering, biology, and mathematics. Students at the school are given the opportunity to experiment with the tools and techniques presented in the lectures.\n\nThe 2014 edition of the Summer School is the fourth in the series takes place during May 19-23, 2014. Topics covered in this school include logic, formalization, interactive theorem proving, SAT and SMT solving, model checking, program semantics, modeling and verification of security protocols, and software reliability. The students cooperate in solving challenging problems while learning to use cutting-edge tools and techniques.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "4352", "attributes": { "award_id": "1458514", "title": "Geoscience Scholarships to Improve Recruitment and Retention of Academically Talented Students", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [ { "id": 14805, "first_name": "Keith", "last_name": "Sverdrup", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2015-04-01", "end_date": "2021-03-31", "award_amount": 639136, "principal_investigator": { "id": 14809, "first_name": "Amy", "last_name": "Sheldon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1269, "ror": "", "name": "SUNY College at Geneseo", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 14806, "first_name": "Dori J", "last_name": "Farthing", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 14807, "first_name": "Scott D", "last_name": "Giorgis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 14808, "first_name": "Nicholas H", "last_name": "Warner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1269, "ror": "", "name": "SUNY College at Geneseo", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This project at SUNY Geneseo will address the national need for more and better trained geoscientists by increasing recruitment, retention to graduation, and preparation for and placement in careers or geosciences graduate programs and by combining scholarships and academic and career services. Through many student support and enrichment activities, this project will enhance interactions between SUNY Geneseo and academic institutions including SUNY Buffalo and SUNY Binghamton where many new geosciences graduates from the college pursue M.S. or Ph.D. degrees. In addition, connections to regional geosciences industries, including the American Rock Salt Company and Stell Environmental Enterprises, as well as government and academic research programs such as NASA, DOE, and a variety of National Laboratory and Research Experience for Undergraduates programs, will be strengthened through the development of opportunities for student research and internships. Together, these improvements will increase the number and quality of research opportunities for undergraduate geosciences students, increase opportunities for students to participate and present at professional and scholarly conferences, and address the national need to increase the number of geosciences students to fill jobs in oil and gas, environmental service, and mining industries. \n\nThe project will target students majoring in geology, geochemistry, and geophysics at SUNY Geneseo and is designed to meet three objectives: (1) increase recruitment and enrollment of academically talented students with financial need by at least 20%, (2) enhance retention and graduation within four years by at least 10%, and (3) increase placement in a geosciences or related science, technology, engineering, or mathematics (STEM) career or graduate program by 13%. The project leadership will work with the Office of Admissions and twenty alumni who are geoscience teachers at high schools in western New York to recruit academically talented scholars with financial need to the program. Cohorts of scholars will be brought together by (a) taking the same classes, (b) engaging in supplemental instruction programs, (c) participating in field trips, (d) interacting with graduate students and alumni, and (v) engaging in research and/or internships. The program goals will be accomplished through two primary components: (i) enhanced student support programs, and (ii) experiential learning opportunities. Career placement-related program components will be supplemented with support from the Office of Career Development. The scholarship program will allow SUNY Geneseo to implement and assess support services for geosciences students, including Supplemental Instruction (SI), Workshops with Graduate Students, a Geology Alumni-Student Program, field trips, research experiences, and internships. SI is a proven method to increase retention and graduation rates in many STEM disciplines; however, the effectiveness of SI has not been tested in the geosciences. This program will fill that void and provide insight into best practices and effective measures for promoting retention, graduation, and placement in the geosciences.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "3584", "attributes": { "award_id": "1654828", "title": "Collaborative Research: The Impact of Face-to-Face and Remote Interviewing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "LSS-Law And Social Sciences" ], "program_reference_codes": [], "program_officials": [ { "id": 11632, "first_name": "Reginald", "last_name": "Sheehan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2017-05-01", "end_date": "2021-04-30", "award_amount": 145095, "principal_investigator": { "id": 11634, "first_name": "Debra", "last_name": "Poole", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1001, "ror": "https://ror.org/02xawj266", "name": "Central Michigan University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 11633, "first_name": "Christopher", "last_name": "Davoli", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1001, "ror": "https://ror.org/02xawj266", "name": "Central Michigan University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite widespread dissemination of best-practice standards for conducting forensic interviews, many jurisdictions lack the expertise to skillfully investigate crimes involving child witnesses. An efficient way to ensure that all jurisdictions have access to highly trained child interviewers is to conduct remote (live-streaming video) forensic interviews. Remote interviewing could reduce investigative response time, spare investigative resources, and accelerate case disposition. However, the ability of remote interviewing to elicit eyewitness evidence from children has not been sufficiently tested and, therefore, will certainly prompt challenges regarding children?s testimonial reliability. The current project is a comprehensive and theoretically grounded evaluation of the effectiveness of remote interviewing of child witnesses. Results will be disseminated to scientists and forensic professionals through publications and presentations, thereby informing policies and guidelines for the use of remote forensic interviews with children. Because remote interviewing increases access to specialized expertise, project results will also impact how children are questioned by electronic means in non-forensic contexts. The project will provide research training to dozens of students at two research sites and promote greater awareness of evidence-based practice through outreach to practitioners who work with child witnesses. \n\nUsing an established paradigm that produces salient touching experiences, individual children at two sites (ages 4 to 8 years) will be told that a male assistant can no longer touch their skin when he delivers a germ education program. The assistant will touch each child once and realize an impending mistake before he completes a second touch. Afterward, children will hear a story from their parents that contains misinformation about the experience, including narrative about a nonexperienced touch. During interviews conducted in traditional face-to-face or remote formats, children will answer questions about the germ education event and answer a series of questions that tests their ability to distinguish experienced from suggested events. By comparing the completeness and accuracy of children?s testimonies across formats, this study will determine whether remote interviewing elicits testimony that is comparable in quality to the testimony elicited by face-to-face interviewing. Measures of behavioral inhibition and executive function will determine whether remote interviewing is beneficial for children who are behaviorally inhibited or contraindicated for typically-developing children who have poor cognitive control.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9472", "attributes": { "award_id": "6U48DP006382-02M002", "title": "Connecting Behavioral Science to COVID-19 Vaccine Demand (CBS-CVD) Network Supplement for PRC - Increasing Effective Mental Health Care for LGBT Clients", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2019-09-30", "end_date": "2024-09-29", "award_amount": 500000, "principal_investigator": { "id": 25187, "first_name": "BRADLEY O", "last_name": "BOEKELOO", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1021, "ror": "", "name": "UNIV OF MARYLAND, COLLEGE PARK", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1021, "ror": "", "name": "UNIV OF MARYLAND, COLLEGE PARK", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": null, "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 2, "pages": 1392, "count": 13920 } } }