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HDR DSC: Collaborative Research: Connecting the Dots

HDR DSC: Collaborative Research: Connecting the Dots
HDR DSC:协作研究:连接点
批准号:
1924292
负责人:
Jeffrey Errington
金额:
$74.79万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-10-01 至 2024-09-30

项目摘要

项目成果

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中文摘要
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英文摘要
There is significant demand for a workforce that is proficient in data science and analytics. Employers seek graduates with an ability to (1) understand, interpret, and analyze data, (2) effectively communicate results that stem from the analysis of data, (3) practice the ethical use of data, and (4) apply data science concepts to solve practical problems with real-world relevance. Data from job search sites indicate that the demand in New York State is particularly acute. A 2018 report from the National Academies of Sciences, Engineering, and Medicine entitled "Data Science for Undergraduates: Opportunities and Options" calls for institutions to advance the so-called "data acumen" of graduates. While the dissemination of data science competencies has been emphasized in some disciplines (e.g., computer science), the broad delivery of these skills to college graduates has been slow to evolve. The aim of this project is to develop and implement a scalable, innovative program, termed "Connecting the Dots", for delivery of data science competencies to students pursuing an undergraduate engineering degree.Connecting the Dots (CTD) is a highly collaborative project between the flagships schools in the State University of New York (SUNY) system, the largest higher education system in the nation, and the City University of New York (CUNY) system. CTD teams the University at Buffalo (UB) with the City College of New York (CCNY) with the goals to (a) strengthen the ability to understand and use data effectively to inform decisions among diverse undergraduate students from across the engineering disciplines, while (b) simultaneously increasing the capacity of regional community partners to incorporate data analytical methods into their business or strategic planning objectives. The signature academic data science track to be created by the CTD project team is an undergraduate certificate program, the New York Data Science Scholars program, that is readily integrated with any engineering major and that complements existing computer science majors at both the undergraduate and graduate level. A broad range of community partners are served via novel Data Science Community Labs, which act as "pop-up" summer facilities on the UB and CCNY campuses wherein students perform internship projects for community partners who have challenging data science problems for students to work on, but are not well-positioned to host a conventional intern. The team's ultimate scaling objective is to develop a program that is easily adopted by other SUNY and CCNY campuses that host 4-year engineering programs and by campuses outside of New York State with similar degree program structures. NSF's Harnessing the Data Revolution Data Science Corps program focuses on building capacity for harnessing the data revolution at the local, state, national, and international levels to help unleash the power of data in the service of science and society. Projects in this program are being jointly funded by the NSF's Harnessing the Data Revolution Big Idea; the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems; the Directorate for Education and Human Resources, Division of Undergraduate Education; the Directorate for Mathematical and Physical Sciences, Division of Mathematical Sciences; and the Directorate for Social, Behavioral and Economic Sciences, Office of Multidisciplinary Activities and Division of Behavioral and Cognitive Sciences.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.
期刊论文(1)
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科研奖励(0)
会议论文
DOI: --
发表时间: 2023
期刊: Proceedings of the American Society of Engineering Education
影响因子: --
作者: [Moore, Kristen, Folks, Leisl, Rowley, Erin.]
通讯作者: Rowley, Erin.
Participant Support for the Eighth Triennial Conference on Foundations of Molecular Modeling and Simulation (FOMMS 2022)
  • 批准号:
    2224189
  • 项目类别:
    Standard Grant
  • 资助金额:
    $4.84万
  • 财政年份:
    2022
  • 负责人:
    Jeffrey Errington
  • 依托单位:
Development of Molecular Simulation Methods to Compute Phase and Interfacial Properties of Complex Fluids
  • 批准号:
    1900344
  • 项目类别:
    Standard Grant
  • 资助金额:
    $39.44万
  • 财政年份:
    2019
  • 负责人:
    Jeffrey Errington
  • 依托单位:
Molecular Simulation Study of Rock-Water-Oil Systems
  • 批准号:
    1705620
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2018
  • 负责人:
    Jeffrey Errington
  • 依托单位:
Development and Application of Molecular Simulation Methods to Compute Bulk and Interfacial Properties of Ionic Liquids
  • 批准号:
    1362572
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $40.5万
  • 财政年份:
    2014
  • 负责人:
    Jeffrey Errington
  • 依托单位:
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  • 项目类别:
    面上项目
  • 资助金额:
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    2023
  • 负责人:
    戴宇翔
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基于DSC-MRI、DCE-MRI及DKI生理参数与ZEB1表达的关联机制实现复发胶质母细胞瘤ZEB1表达可视化的研究
  • 批准号:
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  • 项目类别:
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  • 资助金额:
    30万元
  • 批准年份:
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  • 负责人:
    王宝
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