Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Avenue, Los Angeles, CA, 90033, USA.
McGovern Institute for Brain Research, MIT Brain and Cognitive Sciences, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA.
Sci Data. 2023 Oct 19;10(1):719. doi: 10.1038/s41597-023-02614-0.
As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.
随着数据共享变得越来越普遍，档案、标准和分析工具这三个支柱已经成为促进有效数据共享和协作的关键组成部分。本文比较了四个免费提供的颅内神经电生理学数据存储库：大脑倡议数据档案（DABI）、神经生理学数据集成分布式档案（DANDI）、OpenNeuro 和 Brain-CODE。本综述的目的是描述那些为研究人员提供工具的档案，这些工具可以根据神经科学界感兴趣的标准来存储、共享和重新分析人类和非人类神经生理学数据。这些存储库使用脑成像数据结构（BIDS）和无边界神经数据（NWB），通过实施通用标准，使数据更便于研究人员使用。随着神经科学界不断需要将大规模分析整合到数据存储库平台中，本文将重点介绍所选存储库中开发的各种分析和可定制工具，这些工具可能会推动神经信息学领域的发展。
