Methods for Analyzing Large Neuroimaging Datasets
Robert Whelan (u. a.)
Sold by preigu, Osnabrück, Germany
AbeBooks Seller since August 5, 2024
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Add to basketSold by preigu, Osnabrück, Germany
AbeBooks Seller since August 5, 2024
Condition: New
Quantity: 5 available
Add to basketMethods for Analyzing Large Neuroimaging Datasets | Robert Whelan (u. a.) | Taschenbuch | Neuromethods | xi | Englisch | 2025 | Springer | EAN 9781071642627 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Seller Inventory # 135279066
This Open Access volume explores the latest advancements and challenges in standardized methodologies, efficient code management, and scalable data processing of neuroimaging datasets. The chapters in this book are organized in four parts. Part One shows the researcher how to access and download large datasets, and how to compute at scale. Part Two covers best practices for working with large data, including how to build reproducible pipelines and how to use Git. Part Three looks at how to do structural and functional preprocessing data at scale, and Part Four describes various toolboxes for interrogating large neuroimaging datasets, including machine learning and deep learning approaches. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.
Authoritative and comprehensive, Methods for Analyzing Large Neuroimaging Datasets is a valuable resource that will help researchers obtain the practical knowledge necessary for conducting robust and reproducible analyses of large neuroimaging datasets.
This Open Access volume explores the latest advancements and challenges in standardized methodologies, efficient code management, and scalable data processing of neuroimaging datasets. The chapters in this book are organized in four parts. Part One shows the researcher how to access and download large datasets, and how to compute at scale. Part Two covers best practices for working with large data, including how to build reproducible pipelines and how to use Git. Part Three looks at how to do structural and functional preprocessing data at scale, and Part Four describes various toolboxes for interrogating large neuroimaging datasets, including machine learning and deep learning approaches. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.
Authoritative and comprehensive, Methods for Analyzing Large Neuroimaging Datasets is a valuable resource that will help researchers obtain the practical knowledge necessary for conducting robust and reproducible analyses of large neuroimaging datasets.
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