RNA-seq data on leaf 8 blade tissue of 62 individual Brassica napus (Darmor) plants grown in the field.

62 individual Brassica napus plants of the same accession grown in the same field were expression-profiled in autumn 2016 and phenotyped extensively until harvest in spring 2017. Machine learning models were used to link gene expression to the phenotypes of individual plants, with the purpose of assessing how much phenotype information in encoded in 'noisy' gene expression variation among individual plants of the same background grown under the same uncontrolled field conditions. Rosette leaf 8 blades of 62 individual Brassica napus plants of the same winter-type accession (BnASSYST-120, Darmor) grown in the same field (50°58'24.9\"N 3°46'49.1\"E, Merelbeke, Belgium) were RNA-seq profiled. No treatments or stresses were applied, all plants were profiled individually under uncontrolled field conditions. Sown at 2016-09-08, rosette leaf 8 sampled for RNA-seq at 2016-11-28, plants harvested at 2017-06-13.

2016年秋季，对种植于同一块田间的同一甘蓝型油菜（Brassica napus）种质（accession）的62株个体植株开展表达谱分析，并于2017年春季收获前对其进行了全面的表型鉴定。本研究借助机器学习模型建立基因表达与单株表型的关联，旨在评估在相同非受控田间条件下种植的同一遗传背景植株群体中，“噪声”化的个体间基因表达变异所编码的表型信息占比。针对种植于比利时梅勒贝克（Merelbeke）、坐标为50°58'24.9"N 3°46'49.1"E的同一块田间的62株该冬性甘蓝型油菜（Brassica napus）冬性种质（winter-type accession，BnASSYST-120，Darmor）的第8轮生叶（rosette leaf 8）叶片，开展了RNA测序（RNA-seq）表达谱分析。本实验未施加任何处理或胁迫，所有植株均在非受控田间条件下单独完成了表达谱检测。植株于2016年9月8日播种，第8轮生叶的RNA测序取样时间为2016年11月28日，植株于2017年6月13日收获。