CoPe EAGER: Collaborative Research: COMET: the Coastlines and people Open data and MachinE learning sprinT

CoPe EAGER:协作研究:COMET:海岸线和人类 开放数据和机器学习冲刺

基本信息

  • 批准号:
    2102126
  • 负责人:
  • 金额:
    $ 6.26万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-10-01 至 2023-08-31
  • 项目状态:
    已结题

项目摘要

Timely release of research results is important to advance understanding of the impacts of climate change and sea level rise on coastlines and the communities that live there. The growing library of open, freely accessible data and analysis tools (i.e., code) enables scientists to investigate a range of societally relevant questions at the intersection of Coastlines and People. This project pilots an incubator approach to catalyzing data-driven research and creating networks of researchers ready to tackle the complex problems of the coast. The program is modeled on other 'science sprints', where teams of researchers assemble to transform an idea into open, freely accessible research products within a short, fixed time window - thereby accelerating scientific advances. This project will advance science in three ways: 1) By creating cohorts of scientists using data-driven approaches to address the interdisciplinary problems along the coast; 2) Scientists at each event will create open tools, code, deliverables and data products, creating freely available methods and knowledge; 3) Multiple events and iteration between events will enable evaluation of the sprint approach, and its success in producing science at the intersection of Coastlines and People.The three sprint events are focused on quick turn-around research using open data and machine learning, and will take advantage of the vast data volumes available through data.gov and other FAIR (Findable, Accessible, Interoperable, Re-usable) sources. The objective is to produce results and deliverables rapidly. Applications from the scientific community will be solicited for each of the three planned events, and selection of cohorts will prioritize having representation of a diverse set of fields and perspectives. Each event will adhere to a Code of Conduct that will additionally include an 'open by default' statement for code, data, and reports generated at the sprint. At each event, participants will break into small groups to spend 72 hours working on selected projects. Groups will produce oral and written reports, as well as associated open source code at the end of each event. Outcomes from each event will be measured using surveys (pre- and post- event), and by following the use of digital object identifiers associated with the open deliverables from each event. Datasets used by the participants will also be collected and curated on a publicly available website as a crowd-sourced list of relevant open data. The three sprint events will take place in North Carolina and Colorado. A range of external collaborators will interact and network with participants.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.
及时发布研究结果对于加深了解气候变化和海平面上升对海岸线和居住在那里的社区的影响非常重要。不断增长的开放、可免费访问的数据和分析工具(即代码)库使科学家能够研究海岸线和人类交汇处的一系列与社会相关的问题。该项目试点了一种孵化器方法,以促进数据驱动的研究,并创建准备解决海岸复杂问题的研究人员网络。该计划以其他“科学冲刺”为蓝本,研究人员团队聚集在一起,在短时间内将一个想法转化为开放的、可免费获取的研究产品,从而加速科学进步。该项目将以三种方式推进科学发展:1)通过创建使用数据驱动方法的科学家群体来解决沿海地区的跨学科问题; 2)每次活动的科学家将创建开放的工具、代码、可交付成果和数据产品,创造可免费使用的方法和知识; 3) 多个事件和事件之间的迭代将能够评估冲刺方法及其在海岸线和人类交叉点上产生科学的成功。三个冲刺事件的重点是使用开放数据和机器学习的快速周转研究,以及将利用 data.gov 和其他 FAIR(可查找、可访问、可互操作、可重复使用)来源提供的大量数据。目标是快速产生结果和可交付成果。这三项计划中的活动均将征求科学界的申请,并且选择的群体将优先考虑代表不同领域和观点的情况。每个活动都将遵守行为准则,其中还包括针对冲刺中生成的代码、数据和报告的“默认开放”声明。在每次活动中,参与者将分成小组,花 72 小时致力于选定的项目。小组将在每次活动结束时制作口头和书面报告以及相关的开源代码。 每个活动的成果将通过调查(活动前和活动后)以及与每个活动的开放交付成果相关的数字对象标识符的使用来衡量。参与者使用的数据集也将在公开网站上收集和整理,作为相关开放数据的众包列表。三场短跑比赛将在北卡罗来纳州和科罗拉多州举行。一系列外部合作者将与参与者互动和建立联系。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Prototyping a collaborative data curation service for coastal science
沿海科学协作数据管理服务原型
  • DOI:
    10.1139/anc-2021-0002
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    2.4
  • 作者:
    Goldstein, Evan B.;Braswell, Anna E.;McShane, Caitlin M.
  • 通讯作者:
    McShane, Caitlin M.
Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets
  • DOI:
    10.1016/j.envsoft.2021.105113
  • 发表时间:
    2021-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    C. Kelleher;A. Braswell
  • 通讯作者:
    C. Kelleher;A. Braswell
An Active Learning Pipeline to Detect Hurricane Washover in Post-Storm Aerial Images
用于检测风暴后航空图像中飓风冲刷的主动学习管道
NSF supported socio-environmental research: how do crosscutting programs affect research funding, publication, and citation patterns?
NSF 支持社会环境研究:交叉项目如何影响研究经费、出版和引用模式?
  • DOI:
    10.5751/es-13281-270325
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    4.1
  • 作者:
    Kaiser, Kendra E.;Braswell, Anna E.;Fork, Megan L.
  • 通讯作者:
    Fork, Megan L.
Labeling Poststorm Coastal Imagery for Machine Learning: Measurement of Interrater Agreement
  • DOI:
    10.1029/2021ea001896
  • 发表时间:
    2021-09-01
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    Goldstein, Evan B.;Buscombe, Daniel;Williams, Hannah E.
  • 通讯作者:
    Williams, Hannah E.
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Anna Braswell其他文献

Anna Braswell的其他文献

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{{ truncateString('Anna Braswell', 18)}}的其他基金

CoPe EAGER: Collaborative Research: COMET: the Coastlines and people Open data and MachinE learning sprinT
CoPe EAGER:协作研究:COMET:海岸线和人类 开放数据和机器学习冲刺
  • 批准号:
    1940006
  • 财政年份:
    2019
  • 资助金额:
    $ 6.26万
  • 项目类别:
    Standard Grant

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相似海外基金

CoPe EAGER: Collaborative Research: A GeoAI Data-Fusion Framework for Real-Time Assessment of Flood Damage and Transportation Resilience by Integrating Complex Sensor Datasets
CoPe EAGER:协作研究:GeoAI 数据融合框架,通过集成复杂的传感器数据集实时评估洪水损失和运输弹性
  • 批准号:
    2052063
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    Standard Grant
CoPe EAGER: Collaborative Research: A GeoAI Data-Fusion Framework for Real-Time Assessment of Flood Damage and Transportation Resilience by Integrating Complex Sensor Datasets
CoPe EAGER:协作研究:GeoAI 数据融合框架,通过集成复杂的传感器数据集实时评估洪水损失和运输弹性
  • 批准号:
    1940230
  • 财政年份:
    2020
  • 资助金额:
    $ 6.26万
  • 项目类别:
    Standard Grant
CoPe EAGER: Collaborative Research: A GeoAI Data-Fusion Framework for Real-Time Assessment of Flood Damage and Transportation Resilience by Integrating Complex Sensor Datasets
CoPe EAGER:协作研究:GeoAI 数据融合框架,通过集成复杂的传感器数据集实时评估洪水损失和运输弹性
  • 批准号:
    1940163
  • 财政年份:
    2020
  • 资助金额:
    $ 6.26万
  • 项目类别:
    Standard Grant
CoPe EAGER: Collaborative Research: A GeoAI Data-Fusion Framework for Real-Time Assessment of Flood Damage and Transportation Resilience by Integrating Complex Sensor Datasets
CoPe EAGER:协作研究:GeoAI 数据融合框架,通过集成复杂的传感器数据集实时评估洪水损失和运输弹性
  • 批准号:
    1940091
  • 财政年份:
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  • 资助金额:
    $ 6.26万
  • 项目类别:
    Standard Grant
CoPe EAGER: Collaborative Research: COMET: the Coastlines and people Open data and MachinE learning sprinT
CoPe EAGER:协作研究:COMET:海岸线和人类 开放数据和机器学习冲刺
  • 批准号:
    1939954
  • 财政年份:
    2019
  • 资助金额:
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  • 项目类别:
    Standard Grant
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