标题： 计算与信息学进展用于神经影像的可重复数据分析 显示英文标题

标题： Computational and informatics advances for reproducible data analysis in neuroimaging

摘要： 科学研究所的可重复性已成为关注的焦点。 我们主张开放性和透明度对于可重复性至关重要，并概述了在人类神经成像社区中出现的开放和透明科学生态系统。 我们讨论了为神经成像数据开发的开放数据共享资源的范围，以及数据标准（特别是脑成像数据结构）在实现大规模神经成像数据集的自动共享、处理和重用中的作用。 我们概述了开源Python语言如何为可重复数据分析和可视化提供数据科学平台的基础。 我们还讨论了软件工程的新进展，如容器化，如何为数据分析提供更高的可重复性基础。 这个新生态系统的出现为许多目前在可重复性方面遇到困难的科学领域提供了范例。 显示英文摘要

摘要： The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data sharing resources that have been developed for neuroimaging data, and the role of data standards (particularly the Brain Imaging Data Structure) in enabling the automated sharing, processing, and reuse of large neuroimaging datasets. We outline how the open-source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.