行动中的公平:Brain-CODE - 一个神经科学数据共享平台,以加速大脑研究.
Brendan Behan1, Francis Jeanson2, Heena Cheema1
1Ontario Brain Institute, Toronto, ON, Canada.
Frontiers in neuroinformatics
|June 5, 2023
概括
脑代码平台运用了FAIR数据原则,以加强神经科学研究. 这有助于有效的健康数据共享,改善学习卫生系统中的疾病理解和患者结果.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 有效的健康研究数据共享对于促进疾病理解,预防,治疗和监测至关重要.
- 一个学习健康系统需要数据是可查找,可访问,可互操作和可重复使用的 (FAIR).
- 自2012年以来,安大略大脑研究所 (OBI) 通过其Brain-CODE平台在神经科学领域开创了FAIR数据共享.
研究的目的:
- 描述 Brain-CODE 平台如何运行神经科学研究的 FAIR 数据原则.
- 突出一个以FAIR为重点的神经信息平台的成功和挑战.
主要方法:
- 可查找:Brain-CODE提供交互工具,用于生成与研究问题一致的特定数据子集.
- 可访问:提供多种数据访问机制,包括元数据访问,安全计算环境访问和数据导出.
- 可互操作:标准化在数据捕获和释放阶段实施,以无集成数据元素.
- 可重复使用:采用质量保证措施和控制来最大限度地提高数据的价值和可重复使用性.
主要成果:
- 脑代码成功运行了FAIR原则,增强了神经科学中的数据共享.
- 该平台提供各种数据访问机制,并促进数据标准化.
- 质量控制措施确保数据的价值和可用于高级研究的可重复使用性.
结论:
- 脑代码平台作为一个模型,用于在神经信息学中实施FAIR数据原则.
- 这种方法通过促进广泛收集和共享神经科学研究数据来支持学习健康系统的发展.
- 解决FAIR数据操作化的挑战是最大限度地提高健康研究数据影响的关键.
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