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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：
Anna Braswell
金额：
$ 6.26万
依托单位：
University of Florida
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

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102126&HistoricalAwards=false
关键词：
CoPe EAGER Collaborative Research COMET

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增进对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的了解是重要的。不断增长的开放、可自由访问的数据和分析工具(即代码)库使科学家能够在海岸线和人的交叉点调查一系列与社会相关的问题。该项目试验了一种孵化器方法，以催化数据驱动的研究，并创建准备好解决沿海复杂问题的研究人员网络。该项目仿照其他“科学冲刺”的模式，即研究团队聚集在一起，在短时间内将一个想法转化为开放的、可免费获取的研究成果，从而加速科学进步。该项目将在三个方面推动科学的发展：1)通过使用数据驱动的方法创建科学家队列，以解决沿海地区的跨学科问题；2)在每次活动中，科学家将创建开放的工具、代码、可交付成果和数据产品，创建免费可用的方法和知识；3)多个活动和活动之间的迭代将使对Sprint方法及其在海岸线和人的交汇处产生科学成果的成功进行评估。三个Sprint活动侧重于利用开放数据和机器学习进行快速周转研究，并将利用通过data.gov和其他公平(可查找、可访问、可互操作、可重复使用)来源获得的大量数据。目标是迅速产生成果和交付成果。将为三个计划的活动中的每一个征求科学界的申请，并将优先选择具有不同领域和视角的代表的队列。每个活动都将遵守行为准则，该准则还将包括一条针对Sprint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，在选定的项目上花费72小时进行工作。小组将在每次活动结束时制作口头和书面报告，以及相关的开放源代码。将使用调查(活动前和活动后)以及使用与每个活动的公开交付成果相关联的数字对象识别符来衡量每个活动的结果。还将收集参与者使用的数据集，并将其作为相关公开数据的众包清单放在一个公开网站上进行整理。这三个短跑项目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网络。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。