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

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，持续的发展必然伴随着对食物、能源和水的需求的增加。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的科学家，他们需要利用不断增长的相关数据洪流的计算技能。这项授予芝加哥大学的国家科学基金会培训生（NRT）奖将培养出在各自学科领域全面扎根的学生，这些学生具备处理和交流食物-能量-水系统所有复杂性所需的计算技能和知识广度。该项目预计将为来自物理、生物和社会科学领域的30（30）名硕士和博士研究生提供培训，其中包括15（15）名受资助的学员，将他们联合起来，共同关注计算和数据分析。该项目的愿景是创建一种跨学科培训的新模式，使学生能够跨领域协作和工作，并应用尖端的计算方法。受训者的教育计划旨在形成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。项目内容包括：(1)每个秋季学期开始前的两周训练营，提供技能培训和介绍跨学科材料，包括计算、数据分析和统计模块；(2)为期一年的核心课程，包括食物-能量-水系统介绍，然后是数据分析实习，学生在跨学科团队中分析数据集；(3)沟通与专业发展培训；(4)国际体验机会；(5)社区建设活动。只要有可能，所有的教育元素都将向芝加哥大学的学生开放。该计划的一个重要目标是改善从代表性不足的群体中招收和留住研究生的情况。最后，为了使教育模式能够传播到其他机构，该项目将定量评估教育计划的效益，并公开传播所有教育材料，以促进其使用。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。通过创新的、基于证据的、与不断变化的劳动力和研究需求相一致的综合培训模式，培训项目致力于在高优先级跨学科研究领域对STEM研究生进行有效培训。

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.