Health Data Nexus:医学领域人工智能研究和教育的开放数据平台。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
January Adams, Rafal Cymerys, Karol Szuster, Daniel Hekman, Zoryana Salo, Rutvik Solanki, Muhammad Mamdani, Alistair Johnson, Katarzyna Ryniak, Tom Pollard, David Rotenberg, Benjamin Haibe-Kains
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引用次数: 0

摘要

我们概述了健康数据Nexus的开发,这是一个数据平台,可以通过基于云的计算环境实现数据存储和访问管理。我们描述了这一安全平台在不断发展的公共部门研究领域的重要性,该领域利用了大量数据,特别是从卫生系统获得的临床数据,以及为三个目标用户群体:数据提供者、研究人员和教育工作者提供有意义的利益的重要性。然后,我们描述了治理实践、技术标准和数据安全的实现,以及构建该平台所需的隐私保护,以及突出该平台在促进数据集获取、新颖研究和托管教育课程、研讨会和数据马拉松方面的优势的示例用例。最后,我们讨论了平台开发的关键原则,强调了灵活使用、协作开发和开源科学的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Health Data Nexus: an open data platform for AI research and education in medicine.

We outline the development of the Health Data Nexus, a data platform that enables data storage and access management with a cloud-based computational environment. We describe the importance of this secure platform in an evolving public-sector research landscape that utilizes significant quantities of data, particularly clinical data acquired from health systems, as well as the importance of providing meaningful benefits for three targeted user groups: data providers, researchers, and educators. We then describe the implementation of governance practices, technical standards, and data security, and the privacy protections needed to build this platform, as well as example use-cases highlighting the strengths of the platform in facilitating dataset acquisition, novel research, and hosting educational courses, workshops, and datathons. Finally, we discuss the key principles that informed the platform's development, highlighting the importance of flexible uses, collaborative development, and open-source science.

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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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