1 Introduction
2 Data Sharing is Important
Data Sharing Importance Quiz
10 attempts allowed
3 Why is there an NIH DMS policy?
Why did the NIH implement the DMS policy Quiz
10 attempts allowed
4 Elements of the DMS Plan
Elements of the DMS Plan Quiz
10 attempts allowed
5 Discussion on Benefits of Data Management and Sharing
6 Defining reproducibility
Defining Reproducibility Quiz
10 attempts allowed
7 Data Privacy
Data Privacy Quiz
10 attempts allowed
8 Creating Clear Documentation
Clear Documentation Quiz
10 attempts allowed
9 Why Documentation is Worth the Time
Documentation is Worth the Time Quiz
10 attempts allowed
10 Organizing your project
Project Organization Quiz
10 attempts allowed
11 Record keeping practices
Record Keeping Quiz
10 attempts allowed
12 Guidelines for Good Metadata
Metadata Quiz
10 attempts allowed
13 Data Storage and Data Repositories
Data Storage and Repositories Quiz
10 attempts allowed
14 Health care data sharing tools
Heath care data sharing tools Quiz
10 attempts allowed
15 References
About this Course
About the Authors
Proactive Data Management and Sharing
This course aims to serve as a starting point to cover the basics of good data management and sharing practices.
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About
About the Course
Research projects are typically cross-disciplinary and contain many types of data in various formats. They often involve multiple collaborators generating data across different sites, with different data standards and infrastructure used to generate those data. Therefore, it is more important than ever to be well-versed in the best practices of data management and sharing. The course is intended for individuals in biomedical science labs and program managers who want to learn the best practices and techniques for data management and sharing. The course covers topics such as: how to create a data management and sharing plan that is consistent with NIH requirements, how to identify a good repository to share your data, how to comply with data privacy regulations, how to write good metadata, and how to follow good reproducibility practices including documentation and project organization techniques and tools.
Disclaimer: This course material is for instructional use only and is not a substitute for legal or ethical advice. The findings and conclusions in this course are those of the authors and do not represent official guidance from the National Institutes of Health.
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Instructor
About the Instructor
Carrie Wright
Carrie Wright is a Senior Staff Scientist at the Fred hutchinson Cancer Center and a member of the Fred Hutchinson Data Science Lab. Previously Carrie was an Assistant Scientist in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a faculty member of the Johns Hopkins Data Science Lab. Her work focuses on making data science and informatics more approachable and accessible. She is also a faculty member of the open case studies project, the Informatics Technology for Cancer Research (ITCR) Training Network (ITN), and a co-founder of the LIBD rstats club, a community designed to encourage others to learn more about R programming and statistics. Learn more about Carrie at https://carriewright11.github.io/.
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Course Material
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