神经科学数据格式、模型、存储库和分析:综述

Sze Wei Fong, Nurfaten Hamzah, N. H. Ahamed Hassain Malim, J. M. Abdullah
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摘要

随着神经技术的发展,人们利用这些新技术开展了大量神经科学研究。尽管世界各地的实验室都产生了大量的神经数据,但这些数据大多没有得到共享,无法帮助不同领域的人们了解神经科学。神经数据共享至关重要,因为它能加快神经科学的发现步伐。有效的数据共享取决于文件或数据格式、高度可重用的数据分析工具和数据存储格式的标准化使用。在这篇综述论文中,我们回顾了神经科学界目前使用的四个领域(数据格式、数据模型、数据存储库和数据分析)。最后,我们将讨论与数据共享相关的几个挑战。
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Neuroscience Data Formats, Models, Repositories and Analytics: A Review
As neurotechnologies have gotten better, a lot of neuroscientific research has been done using these new technologies. Even though labs all over the world produce a lot of neuro-data, most of this data has not been shared to help people from different fields understand neuroscience. The neuro-data sharing is essential because it accelerates the pace of discovery in neuroscience. Effective data sharing will depend on the standardized use of file or data formats, highly reusable data analytics tools, and data storage formats. In this review paper, we review the four domains (data format, data model, data repository, and data analytics) that are currently in use in the neuroscience community. In the end, we are discussing several challenges associated with data sharing.
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