公平数据的好处和困难:植物表型数据的再利用案例
Evangelia A Papoutsoglou1,2, Ioannis N Athanasiadis3, Richard G F Visser1
1Plant Breeding, Wageningen University and Research, Wageningen, The Netherlands.
Scientific data
|July 13, 2023
概括
使植物表型化数据 FAIR (可查找,可访问,可互操作,可重复使用) 改进了数据集成和重复使用. 本研究探讨了FAIR数据实践的好处和挑战,以土豆基因型与环境相互作用为案例研究.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 数据科学数据科学数据科学
背景情况:
- 植物表型化实验产生异质,复杂和记录不良的数据,阻碍了重复使用.
- 社会对营养和作物稳定的需求需要高效的数据整合和重复使用方法.
- FAIR数据原则 (可查找,可访问,可互操作,可重复使用) 为改善数据可访问性和可用性提供了一个框架.
研究的目的:
- 检查使植物表型化数据 FAIR 的影响.
- 研究重复使用和创建FAIR数据的好处和挑战.
- 通过使用土豆基因型与环境的相互作用来证明FAIR数据原则,用于发育特征.
主要方法:
- 模拟了一个科学家发现和整合FAIR表型和环境数据集.
- 应用元数据标准,特别是MIAPPE (关于植物表型化实验的最低信息).
- 分析了土豆发育特征的基因型-环境和QTL-环境相互作用.
主要成果:
- 确定了对现有植物表型化数据集的 FAIRifying 存在重大挑战.
- 通过FAIR数据实践证明了通过FAIR数据实践提高可重复使用性和可重复性的潜力.
- 强调了像MIAPPE这样的元数据标准在 FAIRification 过程中的重要性.
结论:
- 使植物表型化数据公平,对于推进农业研究和满足社会需求至关重要.
- 实施FAIR数据原则需要解决与数据异质性和文档相关的挑战.
- 该研究提供了关于FAIR数据在植物科学中的实际应用和好处的见解.
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