通过我们所有人研究计划的数据沙盒模型,为社区带来回报价值
Elizabeth Cohn1, Frida Esther Kleiman2, Shayaa Muhammad1
1Northwell Health, Institute of Health Systems Science, Manhasset, NY 11030, United States.
概括
我们所有人研究计划的新培训计划显著改善了研究人员对其数据集的理解. 这些入门练习和专业培训有效地让各种研究人员参与健康公平研究.
科学领域:
- 健康 公平 研究 健康 公平 研究
- 生物医学信息学 生物医学信息学
- 数据科学教育数据科学教育
背景情况:
- 我们所有人的研究计划旨在在社区内建立研究能力.
- 为参与者带来回报价值是一个关键目标.
- 促进健康公平研究需要可访问的工具和培训.
研究的目的:
- 描述新的入门练习 (数据沙箱) 和专业培训.
- 引导研究人员使用研究员工作台平台.
- 用"我们所有人"数据集促进健康公平研究.
主要方法:
- 开发一个量身定制的培训计划.
- 包括导向模块. 包括导向模块.
- 通过使用公共数据浏览器和分析工具的概述,纳入了"数据宝藏搜索".
主要成果:
- 参与者在"我们所有人"数据集中的知识前后分数显著增加.
- 研究人员在数据集探索中展示了有效的参与.
- 对培训对启动研究有用性的积极反.
结论:
- 描述的方法有效地引导和熟悉各种研究人员与我们所有人的数据集.
- 培训激发了研究人员的兴趣,并"快速启动"了他们的研究工作.
- 这些举措支持该计划的目标,即建立一个多元化的研究社区.
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