{"title":"图像数据集:利用响应面方法优化非胚胎柑橘组织培养物的生长","authors":"Randall P. Niedz, Eldridge T. Wynn","doi":"10.1016/j.dib.2024.111091","DOIUrl":null,"url":null,"abstract":"<div><div>The data are images of Valencia sweet orange nonembryogenic tissue grown on different culture media that varied in the composition of the mineral nutrients from three experiments. Experiment 1 was a 5-factor d-optimal response surface design of five groupings of the component salts that make up Murashige and Skoog (MS) basal salt medium. Experiment 2 was a 3-factor d-optimal response surface design of extended ranges of factors 1, 2, and 3 from Experiment 1. Experiment 3 was thirteen formulations that were predicted using the prediction model generated from the 5-factor RSM from Experiment 1. The predictions were for two types of growth. One, points were predicted where growth was equal to MS medium (the standard), and two, points predicted with growth greater than MS medium by a minimum of 25%. An image representative of each formulation in each of the experiments makes up the dataset. The data will be useful for 1) visualizing the effects of the diverse mineral nutrient compositions, effects that may not be fully captured with single measure metrics; 2) development of image analysis applications via computer vision and segmentation algorithms for additional insight or for more rapid and possibly accurate assessment of tissue growth and quality; and 3) as an educational resource to learn how to use multifactor experimental designs to assess in vitro growth.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111091"},"PeriodicalIF":1.0000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image dataset: Optimizing growth of nonembryogenic citrus tissue cultures using response surface methodology\",\"authors\":\"Randall P. Niedz, Eldridge T. Wynn\",\"doi\":\"10.1016/j.dib.2024.111091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The data are images of Valencia sweet orange nonembryogenic tissue grown on different culture media that varied in the composition of the mineral nutrients from three experiments. Experiment 1 was a 5-factor d-optimal response surface design of five groupings of the component salts that make up Murashige and Skoog (MS) basal salt medium. Experiment 2 was a 3-factor d-optimal response surface design of extended ranges of factors 1, 2, and 3 from Experiment 1. Experiment 3 was thirteen formulations that were predicted using the prediction model generated from the 5-factor RSM from Experiment 1. The predictions were for two types of growth. One, points were predicted where growth was equal to MS medium (the standard), and two, points predicted with growth greater than MS medium by a minimum of 25%. An image representative of each formulation in each of the experiments makes up the dataset. The data will be useful for 1) visualizing the effects of the diverse mineral nutrient compositions, effects that may not be fully captured with single measure metrics; 2) development of image analysis applications via computer vision and segmentation algorithms for additional insight or for more rapid and possibly accurate assessment of tissue growth and quality; and 3) as an educational resource to learn how to use multifactor experimental designs to assess in vitro growth.</div></div>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":\"57 \",\"pages\":\"Article 111091\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352340924010539\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340924010539","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
这些数据是巴伦西亚甜橙非胚胎组织在不同培养基上生长的图像,这些培养基的矿物质营养成分在三次实验中各不相同。实验 1 是对组成 Murashige 和 Skoog(MS)基础盐培养基的五组盐进行 5 因子 d 最佳响应面设计。实验 2 是对实验 1 中因子 1、2 和 3 的扩展范围进行的 3 因子 d 最佳响应面设计。实验 3 是利用实验 1 的 5 因子 RSM 生成的预测模型预测出的 13 种配方。预测结果有两种类型。第一种是预测生长量等于 MS 培养基(标准)的点,第二种是预测生长量至少比 MS 培养基高 25% 的点。每个实验中每种配方的代表图像构成了数据集。这些数据将有助于:1)直观显示不同矿物质营养成分的影响,单一测量指标可能无法完全捕捉这些影响;2)通过计算机视觉和分割算法开发图像分析应用程序,以获得更多洞察力,或更快速、更准确地评估组织生长和质量;3)作为教育资源,学习如何使用多因素实验设计来评估体外生长。
Image dataset: Optimizing growth of nonembryogenic citrus tissue cultures using response surface methodology
The data are images of Valencia sweet orange nonembryogenic tissue grown on different culture media that varied in the composition of the mineral nutrients from three experiments. Experiment 1 was a 5-factor d-optimal response surface design of five groupings of the component salts that make up Murashige and Skoog (MS) basal salt medium. Experiment 2 was a 3-factor d-optimal response surface design of extended ranges of factors 1, 2, and 3 from Experiment 1. Experiment 3 was thirteen formulations that were predicted using the prediction model generated from the 5-factor RSM from Experiment 1. The predictions were for two types of growth. One, points were predicted where growth was equal to MS medium (the standard), and two, points predicted with growth greater than MS medium by a minimum of 25%. An image representative of each formulation in each of the experiments makes up the dataset. The data will be useful for 1) visualizing the effects of the diverse mineral nutrient compositions, effects that may not be fully captured with single measure metrics; 2) development of image analysis applications via computer vision and segmentation algorithms for additional insight or for more rapid and possibly accurate assessment of tissue growth and quality; and 3) as an educational resource to learn how to use multifactor experimental designs to assess in vitro growth.
期刊介绍:
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