Short Course in Data Science for Environmental Public Health

环境公共卫生数据科学短期课程

• 批准号: 10746327
• 负责人: Ava Marie Hoffman
• 金额: $ 19.95万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-25 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10746327
• 关键词: Address; Applied Skills; Awareness; Big Data; Cancer Center; Code; Collaborations; Communities; Community Health; Companions; Continuing Education; Data; Data Analyses; Data Science; Data Set; Democracy; Development; Discipline; Disparity; Documentation; E-learning; Education; Education Gap; Educational Materials; Educational process of instructing; Environmental Health; Environmental Science; Ethics; Event; Faculty; Feedback; Foundations; Goals; Growth; Hispanic-serving Institution; Historically Black Colleges and Universities; Home; Individual; Institution; Instruction; Lead; Learning; Minority-Serving Institution; Modeling; Participant; Persons; Public Health; Publishing; Reproducibility; Research; Research Personnel; Resources; Science; Scientist; Services; Teacher Professional Development; Technology; Time; Training; Travel; Work; community building; community college; data science education; empowerment; experience; flexibility; health data; health disparity; health equity; improved; instructor; learning materials; lectures; massive open online courses; multimodality; online community; online course; participant retention; peer; peer support; programs; recruit; skills; success; supportive environment; tool; tribal college

Project Summary/Abstract Nearly all science fields have been drastically changed by the big data era, and datasets have the potential to create major breakthroughs in environmental health. However, professional development in data science lags behind for environmental health researchers and practitioners. What training does exist primarily benefits privileged institutions. We propose the Short Course in Data Science for Environmental Public Health to bridge this education gap. Through the Fred Hutchinson Cancer Center Data Science Lab, we will leverage our combined 25-plus year track record of developing educational materials, scalable courses, scalable research experiences, and building communities around data science education to create this multi-modal course. The program, which will empower 30 learners annually, begins with a two-week online course that solidifies R programming foundations. These two weeks will use a combination of didactic lectures on best practices and active hands-on lab activities to practice and engrain programming skills, a model for which the lead instructors have earned recognition for excellence in teaching and successfully used to train over 100 professional learners. Participants will practice new skills one topic at a time to make the content more manageable. This foundation will prepare participants for participating in a three-day in-person intensive “Code-a-thon” where they work on authentic environmental health projects. The Code-a-thon will allow participants to practice data ethics skills in peer code review, reproducibility, and transparency in a supportive environment. Additionally, to ensure that we are responsive to the needs of the diverse participants, we will allow learners a mechanism to provide anonymous feedback throughout and beyond the program. To create scalability, we will adapt a companion Massive Open Online Course (MOOC) so that potentially thousands of participants can benefit. We will also harness the strengths of in-person instruction by creating a yearly training for instructors hoping to reproduce this course in their own institution or community. These efforts will be bolstered by an online data community where participants can support, troubleshoot, and collaborate with peers, as well as monthly reminder newsleęers to help participants retain what they learn. We will work with our existing network of faculty from under-resourced institutions to recruit researchers and faculty specifically from Historically Black Colleges and Universities, Hispanic Serving Institutions, Tribal Colleges and Universities, and Community Colleges to participate in the live course. The course will be offered for free and half of the participants will receive travel stipends to help break down barriers to participation. Throughout, learners will work with relevant health equity datasets with the ultimate goal of understanding and addressing disparities in environmental health equity.

项目摘要/摘要 大数据时代几乎所有科学领域都发生了巨大变化，数据集有可能 在环境健康方面建立重大突破。但是，数据科学的专业发展滞后 在环境健康研究人员和从业者的背后。哪些培训存在主要的好处 特权机构。我们建议环境公共卫生数据科学的简短课程 桥梁这个教育差距。通过弗雷德·哈钦森癌症中心数据科学实验室，我们将利用我们的 共同开发教育材料，可扩展课程，可扩展研究的25年以上的记录 经验，并围绕数据科学教育建立社区，以创建这一多模式课程。这 课程每年将授权30名学习者，从两周的在线课程开始，该课程巩固 编程基础。这两个星期将在最佳实践中使用教学演讲的结合， 积极的动手实验室活动以练习和分工编程技能，这是一个领先的模型 教练在教学方面获得了良好的认可，并成功地用于培训100多个 专业学习者。参与者将一次练习新技能一个主题，以使内容更多 可以管理。该基础将使参与者准备参加为期三天的面对面密集型 他们从事真正的环境健康项目的“代码”。代码 - THON将允许 参与者在同行代码审查，可重复性和透明度中练习数据道德技能 环境。此外，为了确保我们对潜水员参与者的需求做出响应，我们将 允许学习者一种机制在整个程序中提供匿名反馈。创建 可伸缩性，我们将适应一个伴侣大规模的在线课程（MOOC），以便可能成千上万个 参与者可以受益。我们还将通过创建年度一年来利用面对面指导的优势 希望在自己的机构或社区中复制本课程的教师培训。这些效果 在线数据社区将使参与者可以支持，进行故障排除和协作来鲜血 与同龄人以及每月的提醒新闻社，以帮助参与者保持自己的学历。我们将工作 我们现有的来自资源不足的机构的教师网络，以招募研究人员和教职员工 历史悠久的黑人学院和大学，西班牙裔服务机构，部落学院的特殊富裕 和大学和社区大学参加现场课程。该课程将免费提供 一半的参与者将受到旅行压力，以帮助打破参与的障碍。 在整个过程中，学习者将与相关的健康权益数据集合作，以了解理解的最终目标 并解决环境健康公平中的分布。