解决生物医学数据挑战和机会,为大规模数据生命周期提供信息,以加强数据共享,互操作性,分析和利益相关方之间的合作
Vivek Sriram1,2, Ashley Mae Conard3, Ilyana Rosenberg3
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Scientific reports
|February 21, 2025
概括
生物医学研究人员面临着数据挑战. 解决数据质量,共享和协作是改善研究数据生命周期和加速发现的关键.
科学领域:
- 生物医学数据科学是生物医学数据科学.
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 由于数据类型多样化和分析碎片化,生物医学发现面临障碍.
- 现有的数据工作流阻碍了数据质量,共享和协作努力.
研究的目的:
- 在生物医学数据任务中识别和分析常见的痛点.
- 探索提高数据质量,共享,分析和协作策略.
主要方法:
- 对15名生物医学专业人员进行的半结构面试的定性分析.
- 归纳-归纳主题编码,以确定研究挑战和需求中的新兴主题.
主要成果:
- 常见的挑战包括数据采购,验证,应用新的分析技术,导航计算环境和可重复结果的分发.
- 安全的数据共享和协作设施在整个发现过程中至关重要.
- 确定了疼痛点,为优化研究数据生命周期提供了机会.
结论:
- 提供了可行的建议,以应对跨越生物医学发现阶段的多原子数据挑战.
- 改进数据管理和协作可以显著提高生物医学研究的效率和可重复性.
- 解决已识别的痛点对于推动生物医学发现和数据生命周期管理至关重要.
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