Data Science for Neuroimaging

Data Science for Neuroimaging by Ariel Rokem and Tal Yarkoni delves into data science methods and tools, including programming, data management, visualisation, and machine learning, and demonstrates their application to neuroimaging research.

As the field of neuroimaging advances towards data-intensive discovery, researchers must adapt to accessing, managing, and analysing datasets on previously unimaginable scales. With an increasing emphasis on reproducibility and rigorous scientific reporting, there is a demand for higher standards of computational practice. This book provides an introduction to data science specifically tailored for neuroimaging researchers, offering methods, tools, and strategies to facilitate automated, reproducible, and scalable analysis and understanding of data.

Through guided, hands-on explorations of openly available neuroimaging datasets, Data Science for Neuroimaging explains essential data science components such as programming, data management, visualisation, and machine learning. Additionally, it describes their practical application within neuroimaging, ensuring that readers develop broadly applicable data science skills that easily translate to their own research questions.