Integrated Regional Training Program in Environmental Health Sciences

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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.