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
• 负责人: Anna Braswell
• 金额: $ 11.24万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940006&HistoricalAwards=false
• 关键词: CoPe; EAGER; Collaborative; Research; COMET

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促进了解气候变化和海平面上升对海岸线和生活在那里的社区的影响至关重要。不断增长的开放、免费访问的数据和分析工具库（即，代码）使科学家能够调查一系列社会相关的问题，在交叉点的线和人。该项目采用孵化器的方法来催化数据驱动的研究，并创建研究人员网络，以解决沿海地区的复杂问题。该计划以其他“科学冲刺”为模型，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、可自由访问的研究产品，从而加速科学进步。该项目将以三种方式推动科学发展：1）通过使用数据驱动的方法建立科学家群体，以解决沿着的跨学科问题; 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，创造免费提供的方法和知识; 3）多个事件和事件之间的迭代将使sprint方法的评估成为可能，以及它在生产线和人的交叉点的科学方面的成功。三个冲刺活动的重点是使用开放数据和机器学习的快速周转研究，并将利用data.gov和其他FAIR（可查找、可解释、可互操作、可重用）资源提供的大量数据。目标是迅速取得成果和交付成果。将为三项计划中的活动征求科学界的申请，并将优先选择代表不同领域和观点的群体。每个活动都将遵守行为准则，该准则还将包括针对sprint生成的代码、数据和报告的“默认打开”声明。在每次活动中，参与者将分成小组，花72小时完成选定的项目。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）进行衡量，并通过使用与每个活动的开放可交付成果相关的数字对象标识符进行衡量。与会者使用的数据集也将在一个公开网站上收集和整理，作为相关开放数据的众包清单。这三项短跑赛事将在北卡罗来纳州和科罗拉多举行。该奖项反映了NSF的法定使命，并通过使用基金会的知识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

psi-collect: A Python module for post-storm image collection and cataloging

psi-collect：用于风暴后图像收集和编目的 Python 模块

• DOI: 10.21105/joss.02075
• 发表时间: 2020
• 期刊: Journal of Open Source Software
• 作者: Moretz, Matthew;Foster, Daniel;Weber, John;Chowdhury, Rinty;Rafique, Shah;Goldstein, Evan;Mohanty, Somya
• 通讯作者: Mohanty, Somya