Improving Knowledge Discovery Through Development of Big Data to Knowledge Skills Courses and Open Educational Resources
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The document outlines training initiatives supported by NIH grants to enhance data science skills among students and researchers in biomedical fields. It describes the development of open educational resources (OERs) and skills courses aimed at tackling challenges in managing and analyzing extensive data generated in research. Key course topics include data wrangling, scientific communication, and ethical issues in big data, with an emphasis on teaching practical applications throughout the research lifecycle.
Improving Knowledge Discovery Through Development of Big Data to Knowledge Skills Courses and Open Educational Resources
- 1. Nicole Vasilevsky1,2, Shannon McWeeney1, William Hersh1, David A. Dorr1,3, Ted Laderas1, Jackie Wirz4, Bjorn Pederson1, Melissa Haendel1,3 1Department of Medical Informatics and Clinical Epidemiology, 2Ontology Development Group, Library, 3Department of Medicine, 4Graduate Student Affairs, 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 TopicsWhy? 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 definitions 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 Open Educational Resources and Skills Courses Approach 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 fill specific gaps § Teaching students how to ask the question and follow through ① Develop Open Educational Resources (OERS) ② Teach Skills Courses 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://dmice.ohsu.edu/bd2k Improving Knowledge Discovery Through Development of Big Data to Knowledge Skills Courses and Open Educational Resources Intro Course Data After Dark § 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 Advanced Course § Four-evening course in May 2016 § Offered to OHSU students, staff and faculty § Taught advanced topics in of data science Data and Donuts § Two courses were offered in June and July 2016 § Offered to OHSU summer interns § Taught basics 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 | Clinical Standards Related to Big Data 05 | Basic Research Data Standards 06 | Public Health and Big Data 07 | Team Science 08 | Secondary Use (Reuse) of Clinical Data 09 | Publication and Peer Review 10 | Information Retrieval 11 | Version Control and Identifiers 12 | Data annotation and curation 13 | Data Tools and Landscape 14 | Ontologies 101 15 | Data metadata and provenance 16 | Semantic data interoperability 17 | Choice of Algorithms and Algorithm Dynamics 18 | Visualization and Interpretation 19 | Replication, Validation and the spectrum of Reproducibility Semantic data interoperability 20 | Regulatory Issues in Big Data for Genomics and Health Semantic Web data 21 | Hosting data dissemination and data stewardship workshops 22 | Hosting data dissemination and data stewardship workshops 23 | Terminology of Biomedical, Clinical, and Translational Research 24 | Computing Concepts for Big Data 25 | Data modeling 26 | Semantic Web data 27 | Context-based selection of data 28 | Translating the Question 29 | Implications of Provenance and Pre- processing 30 | Data tells a story 31 | Statistical Significance, P-hacking and Multiple-testing 32 | Displaying Confidence and Uncertainty Grey = still under development