NRT: Future Rivers: Training a scientifically innovative, communication savvy STEM workforce for sustaining food-energy-water services in large and transboundary river ecosystems
NRT:未来河流:培训一支具有科学创新精神、善于沟通的 STEM 劳动力,以维持大型跨境河流生态系统中的粮食能源水服务
基本信息
- 批准号:1922004
- 负责人:
- 金额:$ 300万
- 依托单位:
- 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:2019-09-01 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Large freshwater ecosystems are lifelines for a majority of the world's population, providing ecosystem goods and services critical to economies and livelihoods. Despite the important societal and economic benefits of these freshwater systems, the ability to predict impacts from ecosystem change and to evaluate tradeoffs is limited. A better understanding of conceptual and quantitative frameworks for evaluating the physical, biological, and social dynamics that sustain freshwater ecosystem services would allow for better management of these critical resources. This National Science Foundation Research Traineeship (NRT) award to the University of Washington will develop an innovative, culturally-aware STEM workforce fluent in state-of-the-art approaches for sustaining food-energy-water services in large river ecosystems and who are prepared to effectively safeguard ecosystem services for a growing world population. The project is driven by an urgent need for interdisciplinary scientists who can address current and future environmental problems by employing the quantitative tools required to integrate, model, and visualize complex datasets and often conflicting outcomes. The Future Rivers NRT training will include coursework and group activities that emphasize quantitative and interdisciplinary literacy. Students will engage in research that integrates transboundary rivers across the world, spanning a range of human disturbance and regional economic development regimes. The project anticipates training sixty (60) MS and PhD students, including eighteen (18) funded trainees, from disciplines across natural resource science, engineering, social science, health science, and policy.The overall goals of the Future Rivers NRT project are to develop a trained workforce in 21st century quantitative and data science approaches to sustain and safeguard food-energy-water services in large river ecosystems, while researching new ways to better predict impacts and safeguard these resources. The project will use numerical modeling and data science to catalyze new approaches for addressing the grand challenge of achieving sustainability in large Food, Energy, and Water Systems (FEWS). To integrate the research and learning, the Future Rivers project follows an active learning model that starts with the presentation of new information, followed by directed practice and exercises, and ends with the application of knowledge gained in a new context. Project training activities are centered around five primary educational objectives: 1) develop new technical and data science skills; 2) foster innovative interdisciplinary and international science integration; 3) improve trainee communication skills; 4) increase cultural awareness and inclusivity among faculty, trainees, and participants; and 5) create networks and opportunities for student career development. Specific project components focus on data science training and careers (courses, hackathon events, research summits, career fairs); communication and outreach skillsets (workshops, science communication film contests); equity and inclusivity training; and interdisciplinary river FEWS issues (courses, seminar series, and summer institutes that include some international locations).The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.
大型淡水生态系统是世界上大多数人口的生命线,提供对经济和生计至关重要的生态系统产品和服务。尽管这些淡水系统具有重要的社会和经济效益,但预测生态系统变化的影响和评估权衡的能力有限。更好地了解评价维持淡水生态系统服务的物理、生物和社会动态的概念和数量框架,将有助于更好地管理这些关键资源。这项授予华盛顿大学的国家科学基金会研究培训(NRT)奖将培养一支创新的、具有文化意识的STEM劳动力队伍,他们精通最先进的方法,可以在大型河流生态系统中维持食物-能源-水服务,并准备好为不断增长的世界人口有效地保护生态系统服务。该项目是由跨学科科学家的迫切需求驱动的,他们可以通过使用所需的定量工具来整合、建模和可视化复杂的数据集和经常相互冲突的结果,来解决当前和未来的环境问题。未来河流NRT培训将包括强调定量和跨学科素养的课程作业和小组活动。学生将从事整合全球跨界河流的研究,跨越一系列人类干扰和区域经济发展制度。该项目预计将培养60名硕士和博士研究生,包括18名受资助的学员,他们来自自然资源科学、工程学、社会科学、卫生科学和政策等学科。未来河流NRT项目的总体目标是培养一支训练有素的劳动力队伍,采用21世纪的定量和数据科学方法来维持和保护大型河流生态系统中的食物-能源-水服务,同时研究更好地预测影响和保护这些资源的新方法。该项目将使用数值建模和数据科学来催化新方法,以解决在大型食品、能源和水系统(FEWS)中实现可持续性的重大挑战。为了整合研究和学习,“未来河流”项目采用主动学习模式,从新信息的呈现开始,然后是有指导的实践和练习,最后是在新环境中应用所获得的知识。项目培训活动围绕五个主要教育目标展开:1)培养新的技术和数据科学技能;2)促进跨学科创新和国际科学整合;3)提高学员沟通能力;4)提高教师、学员和参与者之间的文化意识和包容性;5)为学生的职业发展创造网络和机会。具体的项目组成部分侧重于数据科学培训和职业(课程、黑客马拉松活动、研究峰会、招聘会);传播和外联技能(讲习班、科学传播电影竞赛);公平和包容性培训;跨学科的河流FEWS问题(包括一些国际地点的课程,研讨会系列和夏季研究所)。美国国家科学基金会研究实习生(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式,在高优先级跨学科或融合研究领域对STEM研究生进行有效培训。该奖项反映了美国国家科学基金会的法定使命,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
期刊论文数量(0)
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专利数量(0)
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Gordon Holtgrieve其他文献
Ecosystem synchrony: an emerging property to elucidate ecosystem responses to global change
- DOI:
10.1016/j.tree.2024.08.003 - 发表时间:
2024-12-01 - 期刊:
- 影响因子:
- 作者:
Chloé Vagnon;Julian D. Olden;Stéphanie Boulêtreau;Rosalie Bruel;Mathieu Chevalier;Flavien Garcia;Gordon Holtgrieve;Michelle Jackson;Elisa Thebault;Pablo A. Tedesco;Julien Cucherousset - 通讯作者:
Julien Cucherousset
Gordon Holtgrieve的其他文献
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{{ truncateString('Gordon Holtgrieve', 18)}}的其他基金
FEW: River FEWs: Workshop to explore the nexus between food, energy and water in a large international river system ; University of Washington; September, 2015
FEW:河流 FEW:探讨大型国际河流系统中食物、能源和水之间关系的研讨会;
- 批准号:
1541694 - 财政年份:2015
- 资助金额:
$ 300万 - 项目类别:
Standard Grant
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