FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research.

FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research.
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DOI:
10.3389/fninf.2023.1158378
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发表时间:
2023
影响因子:
3.5
通讯作者:
Mikkelsen, Tom
Mikkelsen, Tom
中科院分区:
医学3区
文献类型:
--
作者:
Behan, Brendan;Jeanson, Francis;Cheema, Heena;Eng, Derek;Khimji, Fatema;Vaccarino, Anthony L.;Gee, Tom;Evans, Susan G.;MacPhee, F. Chris;Dong, Fan;Shahnazari, Shahab;Sparks, Alana;Martens, Emily;Lasalandra, Bianca;Arnott, Stephen R.;Strother, Stephen C.;Javadi, Mojib;Dharsee, Moyez;Evans, Kenneth R.;Nylen, Kirk;Mikkelsen, Tom

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在医疗保健生态系统内有效共享健康研究数据可以对疾病理解、预防、治疗和监测的进步产生巨大影响。通过组合和重复使用健康研究数据,可以对患者和人群产生越来越丰富的见解,这些见解反馈到卫生系统中,从而产生更有效的最佳实践和更好的患者治疗结果。为了实现学习健康系统的承诺,数据需要满足可查找性、可访问性、互操作性和可重用性的公平原则。自 2012 年推出 Brain-CODE 平台和服务以来,安大略大脑研究所 (OBI) 率先开展了符合神经科学 FAIR 原则的数据共享活动。在这里,我们描述 Brain-CODE 如何根据公平原则实施数据共享。 Findable—Brain-CODE 为请求者提供了一种交互式的逐项方法,以生成与其研究问题相符的感兴趣的数据片段。可访问—Brain-CODE 提供多种数据访问机制。我们将讨论这些区分元数据访问、Brain-CODE 安全计算环境中的数据访问以及通过导出进行的数据访问的机制。可互操作——标准化发生在数据捕获级别和数据发布阶段,以允许与类似的数据元素集成。可重复使用 - Brain-CODE 实施多项质量保证措施和控制,以最大限度地提高数据的可重复使用价值。我们将重点介绍以 FAIR 为重点的神经信息学平台的成功和挑战,该平台有助于广泛收集和共享学习健康系统的神经科学研究数据。
The effective sharing of health research data within the healthcare ecosystem can have tremendous impact on the advancement of disease understanding, prevention, treatment, and monitoring. By combining and reusing health research data, increasingly rich insights can be made about patients and populations that feed back into the health system resulting in more effective best practices and better patient outcomes. To achieve the promise of a learning health system, data needs to meet the FAIR principles of findability, accessibility, interoperability, and reusability. Since the inception of the Brain-CODE platform and services in 2012, the Ontario Brain Institute (OBI) has pioneered data sharing activities aligned with FAIR principles in neuroscience. Here, we describe how Brain-CODE has operationalized data sharing according to the FAIR principles. Findable—Brain-CODE offers an interactive and itemized approach for requesters to generate data cuts of interest that align with their research questions. Accessible—Brain-CODE offers multiple data access mechanisms. These mechanisms—that distinguish between metadata access, data access within a secure computing environment on Brain-CODE and data access via export will be discussed. Interoperable—Standardization happens at the data capture level and the data release stage to allow integration with similar data elements. Reusable - Brain-CODE implements several quality assurances measures and controls to maximize data value for reusability. We will highlight the successes and challenges of a FAIR-focused neuroinformatics platform that facilitates the widespread collection and sharing of neuroscience research data for learning health systems.
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