在日益复杂的表型数据集中重新评估数据管理.
Cyril Pommier1, Isabelle Alic2, Llorenç Cabrera-Bosquet3
1Université Paris-Saclay, INRAE, BioinfOmics, URGI, 78026 Versailles, France.
Trends in plant science
|October 2, 2025
概括
管理丰富的表型数据集需要平衡数据分析需求与FAIR数据原则. 一个新的方法提出了一个新的方法.
科学领域:
- 植物生物学 植物生物学
- 生物信息学是一种生物信息学.
- 数据科学是数据科学.
背景情况:
- 现型数据集是复杂的,包含各种数据类型,如图像,时间序列和测量.
- 管理这些数据集涉及到相互矛盾的目标:促进分析与使数据重复使用 (FAIR原则).
- 目前的数据分析方法往往导致大量信息丢失.
研究的目的:
- 提出一个新的框架来管理异构的表型数据.
- 为了使原始,合成和计算数据在没有信息丢失的情况下重复使用.
- 通过理论不可知数据组织来支持各种数据分析需求.
主要方法:
- 倡导"sensu stricto 现象数据集"作为上游数据组织层.
- 使用数据科学工具来进行理论不可知数据结构.
- 区分"sensu stricto 现象数据集"和"专用数据集".
主要成果:
- 拟议的"sensu stricto phenomic数据集"保留了所有原始数据,避免了信息丢失.
- 这种上游组织允许多个用户创建定制的"专用数据集".
- 促进了对表型信息的FAIR数据原则的遵守.
结论:
- 一个理论不可知,上游数据组织策略对于表型数据管理至关重要.
- 这种方法提高了数据的可重复使用性,并支持各种分析要求.
- 在遵守 FAIR 数据原则的同时,能够进行可靠的数据分析.
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