FEW: A Workshop to Identify Interdisciplinary Data Science Approaches and Challenges to Enhance Understanding of Interactions of Food Systems and Water Systems

在未来几十年中，世界人口预计将大幅增长，从而增加对粮食、水、能源和其他资源的需求。此外，由于气候变化、城市化以及粮食、能源和其他资源的相互依存和相互关联性，这些资源挑战可能会被放大。此外，由于环境变化、城市化以及粮食、能源和水系统的相互依存和相互关联性，这些资源挑战可能会被放大，而这些系统传统上是独立分析和规划的。这种零碎的方法（例如，生物燃料）到解决一个系统中的问题（例如，能量）导致了意想不到的问题（例如，食品价格上涨）。nexus FEW安全方法的目标是通过理解、欣赏和可视化FEW系统在地方、区域和全球各级的相互联系和相互依赖性来减少这种意外。然而，由于数据收集协议、数据表示标准、完整数据的获取和数据分析工具方面的差异，全球资源可持续管理的联系方法面临重大挑战。此外，FEW系统为新型数据科学研究提供了重大挑战和机遇。虽然数据科学分析方法广泛应用于大型和复杂的系统，例如社交网络，但复杂物理系统（例如，系统的几个系统）已经远远不够。考虑到FEW系统丰富的数据驱动历史，有一个巨大的机会将新的大规模数据分析方法与广泛的气候，水和能源研究社区开发的物理，经验，过程导向，甚至概念知识进行系统整合。此外，数据科学方法需要考虑模型、变量、位置和季节（食物、能源和水系统）之间的依赖关系，以降低产生误导性结果的风险。目前迫切需要大力推进数据科学，实现联系方法的承诺，以应对人口增长、城市化和气候变化带来的社会挑战。拟议的研讨会将聚集来自数据科学和食品，水和能源系统相关领域的思想领袖。本次研讨会将使用FEW nexus pull和数据科学技术push讨论，以确定在理解，欣赏和可视化FEW系统方面的数据科学挑战。前两个连续的会议将探讨这些相反的方向。第二天将在综合会议中确定少数受启发的数据科学重大挑战。具体来说，本次研讨会的目标是创建一个愿景，即数据驱动方法如何为理解FEW系统之间的交互做出重大贡献，以及实现这一愿景需要进行哪些研究。本提案提供了一个详细的里程碑和任务时间表，包括该团队领导愿景研讨会的简历。拟议的研讨会将促进和实现数据科学家与来自学术界、工业界和联邦机构的FEW关系研究人员之间的跨学科伙伴关系，以开发创新的跨学科研究方法，增强对FEW系统之间相互作用的理解、欣赏和可视化。它有可能制定下一代数据科学研究议程，以更好地理解，欣赏和可视化FEW系统之间的相互作用和相互依赖性。研讨会报告将列入博士研究生数据科学课程的阅读清单。将成果融入教育。该报告还将用于专业研究生数据科学学位的劳动力培训。一个关键目标将是在职业阶段、代表性不足的群体、地域和学科（例如，机器学习、数据挖掘、地理空间分析以及粮食、水和能源系统的关系）。

In coming decades, the world population is projected to grow significantly increasing the demand for food, water, energy, and other resources. Furthermore, these resource challenges may be amplified due to climate change, urbanization, and the interdependent and interconnected nature of food, energy, and other resources. Furthermore, these resource challenges may be amplified due to environmental changes, urbanization, and the interdependent and interconnected nature of food, energy and water (FEW) systems, which were traditionally analyzed and planned independently. Such piece-meal approaches (e.g., bio-fuels) to solving problems in one system (e.g., energy) have led to unanticipated problems (e.g., increase in food prices) in other systems. The goal of the nexus FEW security approach is to reduce such surprises by understanding, appreciating and visualizing the interconnections and interdependencies in the FEW system of systems at local, regional and global levels. However, the nexus approach for sustainable management of global resources faces significant challenges due to differences in data collection protocols, data representation standards, access to complete data and data analysis tools. In addition, the FEW system of systems provides major challenges and opportunities for novel data science research. Although data science analysis methods extensively applied to large and complicated systems, such as social networks, data science efforts in complex physical systems (e.g., system of FEW systems) have been far more meager. Given FEW systems' rich data-driven history, there is a tremendous opportunity to systematically integrate novel large-scale data analysis methods with the physical, experiential, process oriented, and even conceptual knowledge that the broad climate, water, and energy research communities have developed. In addition, data science methods need to account for dependence between models, variables, locations and seasons (of food, energy and water systems) to reduce the risk of yielding misleading results.There is a tremendous need to significantly advance data science and realize the promise of the nexus approach to meet societal challenges in the face of population growth, urbanization and climate change. The proposed workshop will gather thought leaders from both data science and the relevant areas of system of food, water, and energy systems. This workshop will use both FEW nexus pull and data science technology push discussions to identify data science challenges in understanding, appreciating and visualizing the FEW systems. The first two successive sessions will explore these opposing directions. The second day will identify FEW inspired data science grand challenges in a synthesis session. Specifically, the goal of this workshop will be to create a vision of how data driven methods could make a significant contribution to understanding interactions between FEW systems and what research is needed to realize that vision. This proposal provides a detailed schedule of milestone and tasks including this team's resume for leading visioning workshops. The proposed workshop will facilitate and enable interdisciplinary partnerships between data scientists and FEW nexus researchers from academia, industry and federal agencies to develop innovative, interdisciplinary research approaches enhancing the understanding, appreciation, and visualization of the interactions between FEW systems. It has potential to formulate next generation data science research agenda towards better understanding, appreciation and visualization of the interactions and interdependencies among FEW systems. Workshop report will be included in reading lists of graduate courses on data science in Ph.D. to integrate the results in education. The report will also be used in professional graduate data science degrees for workforce training. A key goal will be to diversify participation across career stages, under-represented groups, geographies, and disciplines (e.g., machine learning, data mining, geo-spatial analytics, and nexus of food, water and energy systems).