This course explores the critical intersection of reproducibility and data science within computational brain image analysis. Students will learn principles and practical techniques for ensuring research transparency and rigor when working with neuroimaging data. Topics include robust data management, data standardization, version control, automated analysis pipelines, statistical validation, and open science best practices relevant to fMRI and other brain imaging modalities. The course emphasizes hands-on application of data science tools and programming techniques to produce verifiable and shareable neuroimaging research, fostering a deeper understanding of reliable scientific discovery in neuroscience.
1 units · Medical Satisfactory/No Credit
This course explores the critical intersection of reproducibility and data science within computational brain image analysis. Students will learn principles and practical techniques for ensuring research transparency and rigor when working with neuroimaging data. Topics include robust data management, data standardization, version control, automated analysis pipelines, statistical validation, and open science best practices relevant to fMRI and other brain imaging modalities. The course emphasizes hands-on application of data science tools and programming techniques to produce verifiable and shareable neuroimaging research, fostering a deeper understanding of reliable scientific discovery in neuroscience.
Offered in Autumn 2026 at Stanford University.