Couture Curricula - BD2K Data Science Tailored to Your Needs
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The document outlines the development of skills courses by a research team at Oregon Health & Science University, aimed at training students and researchers to manage and analyze biomedical data effectively. It highlights various course topics, such as data wrangling, scientific communication, and the interpretation of data, along with plans for future offerings and workshops. Supported by NIH grants, the initiative is part of the BD2K program focused on enhancing data science education in the research life cycle.
Couture Curricula - BD2K Data Science Tailored to Your Needs
- 1. William Hersh1, David A. Dorr1,2, Melissa Haendel1,3, Nicole Vasilevsky1,3, Bjorn Pederson1, Jackie Wirz1,3, Shannon McWeeney1 1Department of Medical Informatics and Clinical Epidemiology, 2Department of Medicine, 3Ontology Development Group, Library Oregon Health & Science University, Portland, OR Students felt the course Acknowledgements This work is supported by NIH Grants 1R25EB020379-01 and 1R25GM114820-01. Skills Course Topics Why? Course Offerings Defining The Problem Wrangling Data Methods, Tools And Analysis Scientific Communication Data Identification And Resources … ? v Problems amenable to analytics v Importance of question v Team deinitions v Scope v When we do this wrong: methods don't match v Finding the right data v Search methods v Use of metadata v Data management v Exploratory Data Analysis v Data Dictionary v As you touch data, what can go wrong? v Visualization v Matching algorithms to problems v Reporting Findings and Limitations v Giving “Elevator Speech” on ideas of how to approach problem v Critique of related problem Major challenge: how to manage, analyze and interpret vast amounts of data being generated in biomedical research One goal of NIH Big Data to Knowledge (BD2K) initiative: provide training for students and researchers to address this Research team in the Department of Medical Informatics and Clinical Epidemiology (DMICE) is developing skills courses Approach Intro Course Data After Dark OERs and courses connect the dots that help researchers understand how to apply data science techniques in the context of their whole research life cycle § Skills course and OER topics are aimed to ill speciic gaps § Teaching students how to ask the question and follow through ① Develop Open Educational Resources (OERS) ② Teach Skills Courses 19 | Semantic data interoperability 20 | Semantic Web data 21 | Context-based selection of data 22 | Translating the Question 23 | Implications of Provenance and Pre-processing 24 | Data tells a story 25 | Choice of Algorithms and Algorithm Dynamics 26 | Statistical Signiicance, P-hacking and Multiple-testing 27 | Displaying Conidence and Uncertainty 28 | Visualization and Interpretation 29 | Replication, Validation and the spectrum of Reproducibility 30 | Regulatory Issues in Big Data for Genomics and Health 31 | Hosting data dissemination and data stewardship workshops 32 | Guidelines for reporting, publications, and data sharing OER Modules Challenges Scope Images Style Dissemination How to scope generic curricula for different levels of users How to translate diverse teaching styles into general materials How to maximize dissemination while protecting intellectual property How to incorporate images and other copyrighted materials into open resources Available at: http://skynet.ohsu.edu/bd2k Advanced Course § Week long course in Summer 2015 § Offered to interns and undergraduates § Taught basics of data science in the context of the research life cycle § Two-evening course in January 2016 § Offered to OHSU students, staff and faculty § Taught basics of data science § Four-evening course to be offered in May 2016 § Offered to OHSU students, staff and faculty § Teach advanced topics in of data science 01 | Biomedical Big Data Science 02 | Introduction to Big Data in Biology and Medicine 03 | Ethical Issues in Use of Big Data 04 | Terminology of Biomedical, Clinical, and Translational Research 05 | Computing Concepts for Big Data 06 | Clinical Data and Standards Related to Big Data 07 | Basic Research Data Standards 08 | Public Health and Big Data 09 | Team Science 10 | Secondary Use (Reuse) of Clinical Data 11 | Publication and Peer Review 12 | Information Retrieval 13 | Version control and identiiers 14 | Data annotation and curation 15 | Data and tools landscape 16 | Ontologies 101 17 | Data modeling 18 | Data metadata and provenance Couture Curricula - BD2K Data Science Tailored toYour Needs