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来自的顺序:植物育种数字时代的FAIR数据队列的大数据

Abhishek Gogna¹, Daniel Arend¹, Sebastian Beier²

¹Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstraße 3, 06466, Gatersleben, Germany.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik

|September 24, 2025

概括

由于不可互操作的数据集,植物育种研究面临着创新挑战. 采用FAIR数字对象和数据受托人模型可以改善数据共享并促进未来的创新.

科学领域:

植物育种植物育种
农业数据科学农业数据科学
生物信息学是一种生物信息学.

背景情况:

缺乏可互操作的数据集阻碍了植物育种研究的创新.
当前的数据处理实践阻碍了开放,协作和标准化.
FAIR 数字对象提供了一个解决方案,但尚未得到广泛采用.

研究的目的:

为了解决植物育种数据的互操作性危机.
将现有的数据共享实践 (数据队列) 与FAIR数字对象连接起来.
为联合数据共享提出一个数据托管者模型.

主要方法:

确定和定义植物育种中的"数据队列".
将数据队列与FAIR数字对象和研究基础设施联系起来.
为可持续的数据共享制定数据受托人模型.

主要成果:

建立了一个框架,将植物育种数据群连接到FAIR数字对象.
提出了一个数据受托人模型,以促进联合数据共享.
强调数据管理需要是一个持续的过程.

结论:

实施FAIR数字对象和数据受托人模型可以克服数据互操作性问题.
这种方法促进了植物育种中道德,透明和可持续的数据共享.
将数据管理转变为持续的流程将推动该领域的未来创新.

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