标准化调查数据收集以提高可重复性:ReproSchema生态系统的开发和比较评估
Yibei Chen1, Dorota Jarecka1, Sanu Ann Abraham2
1McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States.
Journal of medical Internet research
|July 11, 2025
概括
ReproSchema标准化了调查数据收集,以提高研究可重复性. 这种以图表为中心的框架确保了数据的一致性和跨不同科学学科的互操作性.
科学领域:
- 生物医学和健康信息学
- 数据科学数据科学数据科学
- 研究方法研究方法研究方法学
背景情况:
- 不一致的调查数据收集阻碍了生物医学,临床,行为和社会科学中的可复制性.
- ReproSchema提供了一个以模式为中心的框架,用于标准化,可重复的调查数据收集.
- 它提供了可重复使用的评估库和用于验证和转换的计算工具.
研究的目的:
- 评估ReproSchema在提高研究可复制性和可靠性的有效性.
- 将ReproSchema与12个现有平台进行数据收集一致性比较.
- 为了证明ReproSchema在心理健康,纵向研究和神经成像研究中的应用.
主要方法:
- 描述了ReproSchema的核心组件:基于模式的设计,可重复使用的评估 (>90),以及验证/转换工具.
- 我们将12个平台与14个FAIR原则和8个调查功能进行了比较.
- 将 ReproSchema 应用于 3 个用例:NIMH-Minimal,ABCD/HEAL 和 CB-P-DAS 检查清单.
主要成果:
- ReproSchema满足了所有14个FAIR标准,并支持8个关键调查功能中的6个.
- 在标准化心理健康评估,跟踪纵向数据和创建交互式检查清单方面表现出灵活性.
- 确保与REDCap和FHIR等现有工具的兼容性.
结论:
- ReproSchema通过采用结构化,基于模式的调查数据收集方法来提高研究可重复性.
- 它集成了版本控制,元数据管理和互操作性,以实现一致,兼容的数据.
- 未来的发展将扩大其用于透明,可扩展和可重复的研究的实用性.
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