Integrated Regional Training Program in Environmental Health Sciences

项目摘要 这个补充项目将开发9个1小时的培训模块，旨在装备俄勒冈州 由家长赠款5T32ES007060支持的大学受训人员：" 环境健康科学"与所需的能力和技能，使环境健康 科学（EHS）数据FAIR（可查找、可解释、可互操作和可重用）和AI/ML（人工智能） 智能和机器语言）-准备就绪。这个项目将解决我们目前培训活动中的一个空白 可供目前的前和博士后学员在我们的T32培训计划，我们的超级基金中心 研究经验和培训协调核心，环境健康科学中心专业 发展核心，我们的伞毒理学研究生课程在这里俄勒冈州立大学，我们的区域合作伙伴 培训机构为我们的T32，SRP和EHSC赠款（太平洋西北国家实验室;大学 华盛顿、新墨西哥州大学（主要的HSI）、加州大学戴维斯分校和加州大学伯克利分校）以及我们的培训 地区大学联盟（俄勒冈州大学、波特兰州立大学、俄勒冈州健康科学 大学（OHSU）和14个地区4年制学院和社区学院（包括4个少数民族服务 机构）。我们将聘请一名额外的数据科学家与数据分析部门的科学家合作 OSU定量生命科学中心（CQLS）的研究小组和太平洋西北大学的卡特里娜沃茨博士 国家实验室（俄勒冈州立大学SRP数据管理和分析核心主任-DMAC）开发网络- 包括实时自我评估练习和评价， 参与者9个培训单元将向完成 模块：基本，中级和高级。培训材料将涵盖基本概念 信息科学和“大数据”人工智能，机器学习（AI/ML），以及关键的 设计研究项目以生成符合FAIR要求的EHS数据的重要性。 完成所有三个模块的学员将能够积极参与数据分析过程， 开发、访问和共享符合公平竞争规则的数据集，以供重复使用。每个学习模块将赋予 技能组合涉及以下三个能力类别，其复杂程度不断提高， 复杂性：1）数据集成; 2）多模态数据分析; 3）机器学习和人工智能 智能这些培训材料将在国内和国际上免费提供， 大学网页。

Project summary This supplementary project will develop nine, 1-hour training modules designed to equip Oregon State University trainees supported by parent Grant 5T32ES007060: “Integrated Regional Training Program in Environmental Health Sciences” with the competencies and skills needed to make Environmental Health Sciences (EHS) data FAIR (Findable, Accessible, Interoperable, and Reusable) and AI/ML (Artificial Intelligence and Machine Language) -ready. This project will address a gap in our current training activities available for current pre- and post- doctoral trainees in our T32 Training Program, our Superfund Center Research Experience and Training Coordination Core, and Environmental Health Sciences Center Professional Development Core, our umbrella Toxicology Graduate Program curriculum here at OSU, our regional partner training institutions for our T32, SRP and EHSC grants (Pacific Northwest National Labs; University of Washington, University of New Mexico (a major HSI), UC-Davis and UC-Berkeley) as well as our training consortium of regional universities (Univ of Oregon, Portland State University, Oregon Health Sciences University (OHSU) and 14 regional 4-year colleges and community colleges (including four minority-serving institutions). We will hire one additional data scientist to collaborate with the scientists in the Data Analytics group in our OSU Center for Quantitative Life Sciences (CQLS) and Dr. Katrina Waters at Pacific Northwest National Labs (Director of the OSU SRP Data Management and Analysis Core -DMAC) to develop the web- based asynchronous training modules which will include real-time self-assessment exercises and evaluation by participants. The 9 training modules will provide three levels of expertise to participants completing the modules: Basic, Intermediate, and Advanced. The training materials will cover the basic concepts of information science and `big data' artificial intelligence, and machine learning (AI/ML), and the critical importance of designing their research projects to generate EHS data compatible with FAIR requirements. Trainees completing all three modules will be able to actively participate in the data analytics process of developing, accessing and sharing FAIR-compliant data sets for reuse. The learning modules will each confer skill sets addressing the following three categories of competency at increasing levels of sophistication and complexity: 1) Data integration; 2) Multimodal data analytics; and 3) Machine Learning and Artificial Intelligence. These training materials will be made freely available nationally and internationally on our university web pages.