HDR DSC: Collaborative Research: Modernizing Water and Wastewater Treatment through Data Science Education & Research (MoWaTER)

收集数据比以往任何时候都更容易，但从中提取可操作的信息比以往任何时候都更具挑战性。社会需要一支了解数据科学并能将其应用于创造性解决问题的劳动力。在这个项目中，将设计一个新颖的数据科学入门课程和本科生暑期研究计划，通过将学生与真实的项目和利益相关者联系起来，激励他们在学术研究早期追求数据科学的职业生涯。数据丰富的水和废水处理行业(W/WWT)将提供广泛的易于处理的项目组合。虽然W/WWT行业的数据很容易收集和丰富，但监测、维护和传感器校准的方法落后于最先进的技术。这项资助的工作将创造机会，以批判性地评估当前方法的适宜性，并产生创造性的替代方案。该项目从多元化和代表性不足的学生群体中招募人才，将培养出准备填补“中级”数据科学职位的下一代数据科学家。同时，这个项目将帮助W/WWT设施运营商通过利用他们数据中的信息来降低成本和改善水质。贝勒大学和科罗拉多矿业学院(Mines)的统计学家、计算机科学家和环境工程师将合作(I)开发一个三学分、免先决条件的大二课程；(Ii)组织一个为期五周的数据科学暑期计划；以及(Iii)培养与W/WWT利益相关者和社区学院(CC)的关系。本课程将通过探究驱动的模块介绍数据科学，以吸引以前可能没有考虑过数据科学职业的学生。它将每年在两所大学并行提供，不仅将解决W/WWT设施的问题，还将解决与缺水相关的问题，如气候变化、农业需求和城市化。暑期计划的制定将专注于用数据解决W/WWT问题。将从贝勒、Mines和CC合作伙伴中招募不同的队列。将提供为期一周的编程前编程新兵训练营，以增强技能。PIS将策划和监督旨在培养数据敏锐性、团队合作和沟通能力的团队项目。与城市和农村W/WWT公用事业公司、分散使用W/WWT系统的制造商、W/WWT处理运营商以及学术合作伙伴建立的关系将提供数据和问题背景。所有项目数据都将被很好地记录下来，并免费提供。学生和项目层面的结果将被正式评估，结果将通过发表在同行评议的期刊和会议报告上发布。学生团队开发的优化操作和监控方法将通过教学视频和技术报告向我们的W/WWT利益相关者和农村行业服务组织进行宣传。NSF的利用数据革命数据科学兵团计划侧重于建设能力，以利用地方、州、国家和国际层面的数据革命，帮助释放数据服务于科学和社会的力量。该计划中的项目由美国国家科学基金会利用数据革命大创意、信息和智能系统部门的计算机和信息科学与工程委员会、本科教育部门的教育和人力资源委员会、数学科学部门的数学和物理科学委员会以及社会、行为和经济科学委员会、多学科活动办公室和行为和认知科学部门共同资助。该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Collecting data is easier than ever before, but extracting actionable information from it is more challenging than ever. Society needs a workforce who understands data science and can apply it to creatively solve problems. In this project, a novel, introductory data science course and undergraduate summer research program will be designed to motivate students early in their academic studies to pursue careers in data science by connecting them with authentic projects and stakeholders. The data-rich industry of water and wastewater treatment (W/WWT) will provide an extensive portfolio of tractable projects. Although data are easy to collect and abundant in the W/WWT industry, methods of monitoring, maintenance, and sensor calibration lag behind the state-of-the art. This funded work will create opportunities to critically assess the suitability of current methods and produce creative alternatives. Recruiting from diverse and underrepresented populations of students, this project will produce the next generation of data scientists who are ready to fill "mid-level" data science positions. At the same time, this project will help W/WWT facility operators reduce costs and improve water quality by utilizing the information in their data.Statisticians, computer scientists, and environmental engineers at Baylor University and Colorado School of Mines (Mines) will collaborate to (i) develop a three-credit, prerequisite-free sophomore-level course; (ii) organize a five-week data science summer program; and (iii) cultivate relationships with W/WWT stakeholders and community colleges (CC). The course will introduce data science through inquiry-driven modules to attract students who may not have previously considered a data science career. It will be offered in parallel at both universities each year and will weave not only W/WWT facility problems throughout but also problems associated with water scarcity, such as climate change, agricultural demands, and urbanization. The summer program will be developed with a singular focus on solving W/WWT problems with data. A diverse cohort will be recruited from Baylor, Mines, and CC partners. A one-week pre-program coding boot camp will be offered to bolster skills. PIs will curate and oversee team projects designed to develop data acumen, teamwork, and communication. Established relationships with urban and rural W/WWT utilities; manufacturers of W/WWT systems for decentralized use; W/WWT treatment operators; and academic partners will provide data and problem context. All project data will be well documented and made freely available. The student and program-level outcomes will be formally assessed, with results disseminated through publication in peer-reviewed journals and conference presentations. Methods for optimal operation and monitoring developed by student teams will be publicized through instructional videos and technical reports to our W/WWT stakeholders and rural industry service organizations.NSF's Harnessing the Data Revolution Data Science Corps program focuses on building capacity for harnessing the data revolution at the local, state, national, and international levels to help unleash the power of data in the service of science and society. Projects in this program are being jointly funded by the NSF's Harnessing the Data Revolution Big Idea; the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems; the Directorate for Education and Human Resources, Division of Undergraduate Education; the Directorate for Mathematical and Physical Sciences, Division of Mathematical Sciences; and the Directorate for Social, Behavioral and Economic Sciences, Office of Multidisciplinary Activities and Division of Behavioral and Cognitive Sciences.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.