PDF(Pubmed)
Abstract：
OBJECTIVE: Phenotyping plants in a field environment can involve a variety of methods including the use of automated instruments and labor-intensive manual measurement and scoring. Researchers also collect language-based phenotypic descriptions and use controlled vocabularies and structures such as ontologies to enable computation on descriptive phenotype data, including methods to determine phenotypic similarities. In this study, spoken descriptions of plants were collected and observers were instructed to use their own vocabulary to describe plant features that were present and visible. Further, these plants were measured and scored manually as part of a larger study to investigate whether spoken plant descriptions can be used to recover known biological phenomena.
METHODS: Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants\' (whose personal data are protected for ethical reasons) and volunteers\' observation transcripts, volunteers\' audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected phenotypes (e.g., plant height, ear and tassel features, etc.; measurements and scores). Data were collected during the summer of 2021 at Iowa State University\'s Agricultural Engineering and Agronomy Research Farms.
METHODS: Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants\' (whose personal data are protected for ethical reasons) and volunteers\' observation transcripts, volunteers\' audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected phenotypes (e.g., plant height, ear and tassel features, etc.; measurements and scores). Data were collected during the summer of 2021 at Iowa State University\'s Agricultural Engineering and Agronomy Research Farms.
摘要：
目的：在田间环境中对植物进行表型分析可能涉及多种方法，包括使用自动化仪器和劳动密集型手动测量和评分。研究人员还收集基于语言的表型描述，并使用受控的词汇和结构，如本体，以实现对描述性表型数据的计算，包括确定表型相似性的方法。在这项研究中,收集植物的口头描述，并指示观察者使用自己的词汇来描述存在和可见的植物特征。Further,这些植物被手动测量和评分作为一个更大的研究的一部分，以调查是否口头植物描述可以用来恢复已知的生物现象。
方法：数据包括玉米威斯康星州多样性面板的686种种质的表型观察，和25个携带可见的阳性对照材料，戏剧性的表型。数据包括种植的种质清单，字段布局，数据收集程序，学生参与者（出于道德原因，其个人数据受到保护）和志愿者观察成绩单，志愿者音频数据文件，植物的地面和航空图像，亚马逊云科技方法选择实验数据，和手动收集的表型(例如，植物高度,耳朵和流苏的特点，等。；测量和分数)。数据是在2021年夏季在爱荷华州立大学的农业工程和农学研究农场收集的。
方法：数据包括玉米威斯康星州多样性面板的686种种质的表型观察，和25个携带可见的阳性对照材料，戏剧性的表型。数据包括种植的种质清单，字段布局，数据收集程序，学生参与者（出于道德原因，其个人数据受到保护）和志愿者观察成绩单，志愿者音频数据文件，植物的地面和航空图像，亚马逊云科技方法选择实验数据，和手动收集的表型(例如，植物高度,耳朵和流苏的特点，等。；测量和分数)。数据是在2021年夏季在爱荷华州立大学的农业工程和农学研究农场收集的。
