Learn to create comprehensive data management plans that comply with NIH's new sharing policy requirements.
Learn to create comprehensive data management plans that comply with NIH's new sharing policy requirements.
This practical course guides researchers through the NIH's new data management and sharing policy requirements effective January 2023. Students learn to develop compliant data management plans, identify appropriate repositories, and address implementation challenges. The curriculum covers all essential elements of data management planning, from storage solutions to budgeting considerations, while emphasizing ethical data sharing practices. Designed for grant applicants, the course provides step-by-step guidance for creating effective data management and sharing plans.
Instructors:
English
What you'll learn
Understand NIH's new data management and sharing policy requirements
Identify appropriate data sharing repositories for specific projects
Develop comprehensive data management plans
Address budgeting and implementation challenges
Navigate ethical considerations in data sharing
Create compliant data sharing strategies
Skills you'll gain
This course includes:
9 Minutes PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course provides comprehensive guidance on the NIH's new data management and sharing policy. Through three detailed modules, students learn about policy requirements, plan development, and implementation strategies. The curriculum covers essential elements including data storage solutions, budgeting for data sharing, and ethical considerations. Special emphasis is placed on practical application, helping researchers create effective data management plans for their NIH grant proposals.
About This Course & Introduction to the Policy
Module 1 · 1 Hours to complete
Creating a Data Management and Sharing Plan
Module 2 · 2 Hours to complete
Further Resources & Conclusion
Module 3 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Pioneering Data Science Education and Computational Biology Innovation
Carrie Wright, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center and holds an affiliated faculty position at Johns Hopkins Bloomberg School of Public Health, where she focuses on making data science and computational biology more accessible to diverse audiences. Her expertise spans multiple domains, teaching courses including "AI for Decision Makers," "AI for Efficient Programming," "Avoiding AI Harm," "Best Practices for Ethical Data Handling," "Data Management and Sharing for NIH Proposals," and "Write Smarter with Overleaf and LaTeX." Her distinguished career includes significant contributions as a former Assistant Scientist in Biostatistics at Johns Hopkins and postdoctoral research at the Lieber Institute for Brain Development, where she studied genetic mechanisms in psychiatric disease. As a member of the Open Case Studies team, the Genomic Data Science Community Network, and chair of the ITCR OPEN Group, she demonstrates her commitment to advancing science, medicine, and social justice through accessible data science education. Her innovative work includes co-founding the LIBD rstats club and teaching at various institutions, including the Baltimore Underground Science Space and Johns Hopkins Center for Talented Youth.
Bridging Data Science, Ecology, and Genomics Research
Ava Hoffman, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center, where she leads groundbreaking initiatives in making genomics research more accessible through cloud-based resources. Her unique expertise spans both ecological and data sciences, with particular focus on population genetics and statistical modeling in natural environments. As an instructor for multiple courses including "AI for Decision Makers," "AI for Efficient Programming," "Data Management and Sharing for NIH Proposals," and "Exploring AI Possibilities," she brings a practical approach to complex topics. Her research interests include urban plant evolution and its connection to public health outcomes, particularly studying how plants adapt to city environments. Beyond her academic work, she serves on the board of Slow Food Baltimore, demonstrating her commitment to sustainable practices. Her work with the AnVIL Project and the Genomic Data Science Community Network (GDSCN) showcases her dedication to democratizing genomics research. She combines her technical expertise with practical applications, engaging in activities from coding to climbing, while maintaining a DIY approach to problem-solving.
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