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

HDR DSC：合作研究：通过数据科学教育实现水和废水处理现代化

基本信息

批准号：
1924146
负责人：
Amanda Hering
金额：
$ 115.79万
依托单位：
Baylor University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924146&HistoricalAwards=false
关键词：
HDR DSC Collaborative Research Modernizing

项目摘要

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.

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。社会需要一个了解数据科学并能应用它创造性地解决问题的劳动力。在这个项目中，一个新颖的，介绍性的数据科学课程和本科暑期研究计划将被设计为激励学生在学术研究的早期，通过将他们与真实的项目和利益相关者联系起来，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）将提供广泛的易处理项目组合。虽然数据很容易收集和丰富的W/WWT行业，监测，维护和传感器校准的方法落后于国家的最先进的。这项资助的工作将创造机会，严格评估当前方法的适用性，并产生创造性的替代品。该项目从不同的和代表性不足的学生群体中招募，将培养下一代数据科学家，他们准备填补“中级”数据科学职位。同时，该项目将帮助W/WWT设施运营商通过利用其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（矿业）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，无先决条件的采矿水平课程;（ii）组织一个为期五周的数据科学暑期课程;（iii）组织一个为期五周的数据科学暑期课程;（iv）组织一个为期五周的数据科学暑期课程。及（iii）培养与西/污水处理厂持份者及社区学院的关系。该课程将通过调查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，不仅将贯穿整个W/WWT设施问题，还将涉及与水资源短缺相关的问题，如气候变化，农业需求和城市化。夏季计划将开发一个单一的重点解决W/WWT问题的数据。将从贝勒、矿业和CC合作伙伴中招募一批多样化的人员。一个为期一周的程序前编码靴子营将提供支持的技能。PI将策划和监督旨在开发数据敏锐性，团队合作和沟通的团队项目。与城市和农村W/WWT公用事业建立关系;分散使用的W/WWT系统制造商; W/WWT处理运营商;和学术合作伙伴将提供数据和问题背景。所有项目数据都将有据可查并免费提供。将正式评估学生和方案一级的成果，并通过在同行评审的期刊和会议报告中发表来传播结果。由学生团队开发的最佳操作和监测方法将通过教学视频和技术报告向我们的W/WWT利益相关者和农村工业服务组织进行宣传。NSF的利用数据革命数据科学团计划侧重于在地方，州，国家，和国际水平，以帮助释放数据的力量，为科学和社会服务。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联合资助;信息和智能系统部计算机和信息科学与工程理事会;本科教育部教育和人力资源理事会;数学科学部数学和物理科学理事会;该奖项反映了NSF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。