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NRT INFEWS: computational data science to advance research at the energy-environment nexus

NRT INFEWS：计算数据科学推进能源与环境关系的研究

基本信息

批准号：
1735359
负责人：
Elisabeth Moyer
金额：
$ 299.51万
依托单位：
University of Chicago
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1735359&HistoricalAwards=false
关键词：
NRT INFEWS computational data science

项目摘要

In the near future, humanity will be confronted with unprecedented challenges as we seek to maintain the economic growth that drives prosperity while managing increasing environmental stresses. In particular, continuing development is necessarily accompanied by rising demand for food, energy, and water. Advancing the understanding of these complex and interacting systems requires training a next generation of interdisciplinary scientists with the computational skills required to exploit growing torrents of relevant data. This National Science Foundation Traineeship (NRT) award to the University of Chicago will produce students who are fully grounded in their respective disciplines and who have the computational skills and breadth of knowledge needed to address and communicate the food-energy-water system in all of its complexity. This project anticipates providing training for thirty (30) MS and PhD students, including fifteen (15) funded trainees, from across the physical, biological, and social sciences, uniting them with a common focus on computation and data analysis. The project's vision is to create a new model for interdisciplinary training that gives students the ability to collaborate and work across fields and to apply cutting-edge computational methods. The trainees' educational program is structured to generate a cohesive community of young researchers who have regular, in-depth interactions and opportunities to share expertise across disciplines. Program components include: (1) two-week bootcamps prior to the start of each Fall quarter that provide skills training and introduce cross-disciplinary material, including modules on computing, data analysis, and statistics; (2) a year-long core course sequence consisting of an introduction to the food-energy-water system followed by a data analysis practicum in which students work in interdisciplinary teams to analyze datasets; (3) communication and professional development training; (4) international experience opportunities; and (5) community building activities. All educational elements will be opened to students across the University of Chicago whenever possible. An important goal of the program is to improve the recruitment and retention of graduate students from underrepresented groups. Finally, to enable dissemination of the educational model to other institutions, the project will quantitatively evaluate the benefits of the education program and publicly disseminate all educational material to facilitate its use. 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 Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.

在不久的将来，人类将面临前所未有的挑战，因为我们寻求保持经济增长，推动繁荣，同时管理日益增加的环境压力。特别是，持续发展必然伴随着对粮食、能源和水的需求不断增加。推进对这些复杂和相互作用的系统的理解需要培养下一代跨学科科学家，他们具有利用不断增长的相关数据所需的计算技能。这个国家科学基金会培训（NRT）奖给芝加哥大学将培养学生谁是完全扎根于各自的学科，谁拥有的计算技能和知识的广度需要解决和沟通的食品能源水系统在其所有的复杂性。该项目预计将为30名MS和博士生提供培训，其中包括15名受资助的学员，来自物理，生物和社会科学，将他们团结在一起，共同关注计算和数据分析。该项目的愿景是创建一个跨学科培训的新模式，使学生能够跨领域合作和工作，并应用尖端的计算方法。学员的教育计划旨在产生一个有凝聚力的年轻研究人员社区，他们定期进行深入的互动，并有机会分享跨学科的专业知识。方案组成部分包括：（1）在每个秋季季度开始前进行为期两周的训练营，提供技能培训并介绍跨学科材料，包括计算，数据分析和统计模块;（2）为期一年的核心课程序列，包括介绍食品-能源-水系统，然后是数据分析实习，学生在跨学科团队中工作以分析数据集;（3）沟通及专业发展训练;（4）国际经验机会;及（5）社区建设活动。所有的教育元素将开放给整个芝加哥大学的学生只要有可能。该计划的一个重要目标是改善从代表性不足的群体中招聘和留住研究生。最后，为了能够向其他机构传播教育模式，该项目将定量评估教育方案的效益，并公开传播所有教育材料，以促进其使用。NSF研究培训（NRT）计划旨在鼓励为STEM研究生教育培训开发和实施大胆的，新的潜在变革模式。培训课程致力于在高优先级的跨学科研究领域对STEM研究生进行有效培训，通过全面的培训模式，这些模式具有创新性，以证据为基础，并与不断变化的劳动力和研究需求保持一致。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Large potential for crop production adaptation depends on available future varieties

DOI：
10.1111/gcb.15649
发表时间：
2021-05
期刊：
Global Change Biology
影响因子：
11.6
作者：
F. Zabel;C. Müller;J. Elliott;S. Minoli;J. Jägermeyr;J. Schneider;J. Franke;Elisabeth Moyer;Marie Dury;L. François;C. Folberth;Wenfeng Liu;T. Pugh;S. Olin;Sam S. Rabin;W. Mauser;T. Hank;A. Ruane;S. Asseng
通讯作者：
F. Zabel;C. Müller;J. Elliott;S. Minoli;J. Jägermeyr;J. Schneider;J. Franke;Elisabeth Moyer;Marie Dury;L. François;C. Folberth;Wenfeng Liu;T. Pugh;S. Olin;Sam S. Rabin;W. Mauser;T. Hank;A. Ruane;S. Asseng

Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

DOI：
10.1038/s43016-021-00400-y
发表时间：
2021-11-01
期刊：
NATURE FOOD
影响因子：
23.2
作者：
Jagermeyr, Jonas;Mueller, Christoph;Rosenzweig, Cynthia
通讯作者：
Rosenzweig, Cynthia

Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986–1990

预测低繁殖数下登革热流行的再次出现时间：里约热内卢的 DENV1，1986 年至 1990 年

DOI：
10.1098/rsif.2020.0273
发表时间：
2020
期刊：
Journal of The Royal Society Interface
影响因子：
3.9
作者：
Subramanian, Rahul;Romeo-Aznar, Victoria;Ionides, Edward;Codeço, Claudia T.;Pascual, Mercedes
通讯作者：
Pascual, Mercedes

Global risk of heat stress to cattle from climate change

气候变化对牛造成的全球热应激风险