Collaborative Research: HDR DSC: Building Capacity in Data Science through Biodiversity, Conservation, and General Education

解决环境挑战在很大程度上依赖于应用于大型数据集的计算机科学和统计技能。然而，这些知识、技能和能力在美国的大学、学生或教职员工中并不均匀分布。该项目致力于通过为对城市和荒野地区的保护和管理感兴趣的本科生创建、实施和评估数据科学培训计划来转变环境教育。来自亚利桑那大学和刘易斯与克拉克学院的教员共同教授了一门基础数据科学课程，该课程利用公共数据，解决与学生兴趣和身份相关的紧迫环境问题。杰出的学生随后参加了一个更有针对性的合作课程，作为保护利益相关者的数据科学实习生。针对教学人员的数据科学教育专业发展讲习班，其中一些人在主要服务于低收入学生和有色人种学生的机构工作，为教师在各自机构实施数据科学教育模块做好准备。这些关键资源和途径使广大社区能够利用数据革命。对数据分析师和数据科学家的劳动力需求超过了目前高等教育培养这些熟练劳动力的能力。该项目的总体目标是开发可扩展、便携的数据科学教育，这些教育可以很容易地整合到专注于STEM(科学、技术、工程和数学)的现有项目中，重点放在生态、生物多样性和保护上。该项目通过通识教育课程和基础专业课程，使用文科教育中更常见的包容性和拓展性教学技术，为范围广泛的学生在本科培训早期创建了多个课程数据科学入口，从而实现了这一目标。这些活动的预期结果是：(1)开发可重复使用的数据科学模块和课程，可部署到现有的本科普通教育和主要课程中；(2)使对保护感兴趣的广泛学生能够在教育的早期阶段获得他们热爱的真实世界数据科学培训；以及(3)为希望将数据科学添加到其课程中的本科教育工作者提供培训和支持机制。这一拟议的多机构数据科学团队计划的产品旨在通过开放数据和开放科学普遍扩展到其他高等教育机构和专业，提供快速部署数据科学培训的能力。该奖项反映了NSF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。

Solving environmental challenges relies heavily on computer science and statistical skills applied to large data sets. However, these knowledge, skills, and abilities are not evenly distributed across colleges, students, or faculty in the United States. This project works to transform environmental education by creating, implementing, and evaluating a data science training program for undergraduate students who are interested in conservation and management in urban and wild areas. Faculty from both the University of Arizona and Lewis and Clark College co-teach a foundational data science course that utilizes public data and addresses pressing environmental concerns relevant to student interests and identities. Exceptional students subsequently participate in a more focused cooperative course as data science interns for conservation stakeholders. Professional development workshops in data science education for instructional faculty, some of whom work in institutions serving primarily low-income students and students of color, prepare faculty to implement data science educational modules at their respective institutions. These critical resources and pathways allow broad communities to harness the data revolution.The workforce demand for data analysts and data scientists exceeds the current capacity for higher education to produce this skilled workforce. The overall goal of this project is development of scalable, portable data science education that can be readily incorporated into existing programs concentrating on STEM (science, technology, engineering, and mathematics), with a focus on ecology, biodiversity, and conservation. The project achieves this goal by creating multiple curricular data science on-ramps for a broad range of students early in their undergraduate training, through general education courses and foundational major courses using inclusive and expansive pedagogy techniques more common in liberal arts education. The expected outcomes from these activities are (1) development of reusable data science modules and courses that can be deployed into existing undergraduate general education and major curricula, (2) the ability for a broad range of conservation interested students to access real-world data science training they are passionate about at an early stage of their education, and (3) training and support mechanisms for undergraduate educators who wish to add data science to their curricula. The products of this proposed multi-institutional Data Science Corps program are designed to be generally extensible to other higher educational institutions and majors through open data and open science, providing capacity to rapidly deploy data science training.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.