在多OMICS和AI时代,FAIR数据管理的实用指南
Douaa Mugahid1, Jared Lyon2, Charlie Demurjian2
1Department of Immunology and Infectious Diseases, T.H. Chan School of Public Health, Harvard University, Boston, MA, United States.
Frontiers in immunology
|February 4, 2025
概括
我们提出了一个多模式生物数据管理和FAIR共享的实际框架. 这种方法提高了数据的可访问性和可重复使用性,加速了生物医学发现,并与全球数据政策保持一致.
科学领域:
- 生物医学研究的研究.
- 计算生物学是一种计算生物学.
- 数据科学是数据科学.
背景情况:
- 生物系统是复杂的,需要通过系统生物学进行定量理解.
- 通过新技术,越来越多的高维多模式数据 (多态数据) 越来越容易获得.
- 当前的分析工具和数据共享实践限制了生物学见解和科学合作.
研究的目的:
- 概述多模式生物数据管理的实用框架.
- 促进 FAIR (可查找,可访问,可互操作,可重复使用) 数据共享.
- 通过提高数据的可访问性和实用性来加速科学发现.
主要方法:
- 开发一个实用的数据管理框架.
- 实施公平的数据共享原则.
- 与美国和欧盟资助者数据共享政策保持一致.
主要成果:
- 拟议的框架有助于多模式数据管理.
- 这种方法促进了公平的数据共享,提高了数据的寿命和效用.
- 该战略支持科学界更容易地使用和重复使用数据.
结论:
- 有效的数据管理和公平的共享对于系统生物学至关重要.
- 概述的框架解决了当前数据分析和可访问性的局限性.
- 实施这种方法可以加速生物医学研究和科学进步.
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