Quantitative monitoring of Arabidopsis thaliana growth and development using high-throughput plant phenotyping

Abstract

Abstract With the implementation of novel automated, high throughput methods and facilities in the last years, plant phenomics has developed into a highly interdisciplinary research domain integrating biology, engineering and bioinformatics. Here we present a dataset of a non-invasive high throughput plant phenotyping experiment, which uses image- and image analysis- based approaches to monitor the growth and development of 484 Arabidopsis thaliana plants (thale cress). The result is a comprehensive dataset of images and extracted phenotypical features. Such datasets require detailed documentation, standardized description of experimental metadata as well as sustainable data storage and publication in order to ensure the reproducibility of experiments, data reuse and comparability among the scientific community. Therefore the here presented dataset has been annotated using the standardized ISA-Tab format and considering the recently published recommendations for the semantical description of plant phenotyping experiments.

Plant phenotyping relevance

画像解析による高スループット植物フェノタイピング実験の画像・抽出形質データセットを提示し、再現性とデータ再利用のための標準化記述も扱うため、方法論的役割が中心です。

Code and data availability

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