PDF(Pubmed)
Abstract：
Plant phenotyping experiments are conducted under a variety of experimental parameters and settings for diverse purposes. The data they produce is heterogeneous, complicated, often poorly documented and, as a result, difficult to reuse. Meeting societal needs (nutrition, crop adaptation and stability) requires more efficient methods toward data integration and reuse. In this work, we examine what \"making data FAIR\" entails, and investigate the benefits and the struggles not only of reusing FAIR data, but also making data FAIR using genotype by environment and QTL by environment interactions for developmental traits in potato as a case study. We assume the role of a scientist discovering a phenotypic dataset on a FAIR data point, verifying the existence of related datasets with environmental data, acquiring both and integrating them. We report and discuss the challenges and the potential for reusability and reproducibility of FAIRifying existing datasets, using metadata standards such as MIAPPE, that were encountered in this process.
摘要：
为了不同的目的,在各种实验参数和设置下进行植物表型实验。它们产生的数据是异构的，复杂,通常记录不佳，因此,很难重复使用。满足社会需求(营养，作物适应性和稳定性)需要更有效的数据集成和重用方法。在这项工作中,我们检查“制作数据公平”需要什么，并调查不仅重复使用FAIR数据的好处和斗争，而且以马铃薯的发育性状为案例研究，利用环境基因型和环境相互作用的QTL进行数据公平。我们承担科学家在FAIR数据点上发现表型数据集的角色，用环境数据验证相关数据集的存在，同时获取并整合它们。我们报告并讨论了现有数据集的可重用性和可重复性的挑战和潜力，使用元数据标准，如MIAPPE，在这个过程中遇到的。
