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

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2023-05-18 eCollection Date: 2023-01-01 DOI:10.3389/fninf.2023.1158378
Brendan Behan, Francis Jeanson, Heena Cheema, Derek Eng, Fatema Khimji, Anthony L Vaccarino, Tom Gee, Susan G Evans, F Chris MacPhee, Fan Dong, Shahab Shahnazari, Alana Sparks, Emily Martens, Bianca Lasalandra, Stephen R Arnott, Stephen C Strother, Mojib Javadi, Moyez Dharsee, Kenneth R Evans, Kirk Nylen, Tom Mikkelsen
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引用次数: 0

Abstract

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.

Abstract Image

Abstract Image

FAIR 在行动:Brain-CODE - 加快脑科学研究的神经科学数据共享平台。
在医疗保健生态系统中有效共享健康研究数据可对疾病的理解、预防、治疗和监测产生巨大影响。通过合并和重复使用健康研究数据,可以对患者和人群提出越来越丰富的见解,并反馈到医疗系统中,从而产生更有效的最佳实践和更好的患者治疗效果。为了实现学习型医疗系统的承诺,数据需要符合可查找性、可访问性、互操作性和可重用性的 FAIR 原则。自2012年推出Brain-CODE平台和服务以来,安大略脑研究所(OBI)已率先在神经科学领域开展了符合FAIR原则的数据共享活动。在此,我们将介绍 Brain-CODE 如何根据 FAIR 原则实施数据共享。Findable-Brain-CODE为请求者提供了一种交互式的分项方法,以生成符合其研究问题的感兴趣的数据片段。Accessible-Brain-CODE 提供多种数据访问机制。这些机制包括元数据访问、在 Brain-CODE 的安全计算环境中访问数据以及通过导出访问数据。可互操作--在数据采集层面和数据发布阶段进行标准化,以便与类似的数据元素进行整合。可重用性--Brain-CODE 实施了多项质量保证措施和控制措施,以最大限度地提高数据的重用价值。我们将重点介绍以 FAIR 为重点的神经信息学平台所取得的成功和面临的挑战,该平台可促进神经科学研究数据的广泛收集和共享,为学习型医疗系统提供便利。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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