Data in Brief最新文献

Interactive plant growth regulator and fertilizer application dataset on growth and yield attributes of tomato (Solanum lycopersicum L.) 植物生长调节剂和施肥对番茄（Solanum lycopersicum L.）生长和产量属性的交互式数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-11-14 DOI: 10.1016/j.dib.2024.111136

Joydeb Gomasta, Jahidul Hassan, Hasina Sultana, Emrul Kayesh

Tomato is known for its remarkable contents of vitamins, minerals and antioxidants. A pot experiment was performed during the winter-summer transition from December 2022 to April 2023 combining low to high fertilizer rates and plant growth regulators (PGRs). The objective was to decrease the utilization of artificial fertilizers through the application of PGRs. Besides recommended dose (12 g of urea, 10 g of TSP, 5 g of MoP, 3 g of Gypsum, 0.5 g of ZnSO₄, and 0.5 g of Boric acid per plant), the experiment involved applying fertilizers at 80 %, 90 %, and 110 % of the recommendation plus a control (farmers practice). Furthermore, PGRs including gibberellic acid (GA₃), naphthalene acetic acid (NAA), 4-chlorophenoxy acetic acid (4-CPA) and salicylic acid (SA) were applied at a concentration of 50 ppm. Setting the experiment in a factorial randomized complete block design (RCBD), data on vegetative and reproductive plant behaviors were registered to assess the interactive influence of inorganic nutrients and PGRs on tomato growth and development. The dataset obtained from the experiment focuses on how plant growth regulators like GA₃ and SA significantly ameliorated the reduced chemical fertilizer induced nutrient deficit. Plants had superior growth and yield with GA₃ and SA applications, whereas NAA and 4-CPA accounted for inferior crop health and production even lower than that of control (no PGRs). In addition, as a function of PGR treatment, the tomato plants showed no distinguishable variations in vegetative and reproductive behaviors for fertilizer doses from 80 % to 110 % of recommendation. The dataset, thus, can encourage the farmers, researchers and policymakers for sustainable tomato cultivation with minimal inorganic fertilization through incorporating judicious PGRs. Future studies should focus on cellular and metabolic changes in tomato after PGR-fertilizer interactive use.

众所周知，番茄含有丰富的维生素、矿物质和抗氧化剂。在 2022 年 12 月至 2023 年 4 月的冬夏交替期间进行了一项盆栽试验，结合低肥率到高肥率和植物生长调节剂（PGRs）。目的是通过施用植物生长调节剂来减少人工肥料的使用。除推荐剂量（每株 12 克尿素、10 克 TSP、5 克 MoP、3 克石膏、0.5 克 ZnSO4 和 0.5 克硼酸）外，试验还包括施用推荐剂量 80%、90% 和 110%的肥料以及对照（农民实践）。此外，还施用了浓度为 50 ppm 的 PGRs，包括赤霉素（GA3）、萘乙酸（NAA）、4-氯苯氧乙酸（4-CPA）和水杨酸（SA）。实验采用阶乘随机完全区组设计（RCBD），记录植物的无性和生殖行为数据，以评估无机养分和 PGRs 对番茄生长发育的交互影响。实验所获得的数据集集中反映了 GA3 和 SA 等植物生长调节剂如何显著改善化肥减少引起的养分不足。施用 GA3 和 SA 后，植物的生长和产量均有提高，而施用 NAA 和 4-CPA 后，作物的健康状况较差，产量甚至低于对照组（未施用植物生长调节剂）。此外，作为 PGR 处理的一个函数，番茄植株在肥料剂量为推荐值的 80% 到 110% 时，无性生殖和生殖行为没有明显变化。因此，该数据集可以鼓励农民、研究人员和政策制定者通过合理使用 PGRs，以最少的无机肥料实现番茄的可持续栽培。今后的研究应侧重于番茄在PGR-肥料交互使用后的细胞和代谢变化。

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引用次数: 0

Dataset on developing climate-smart transplanted Aus rice variety BRRI dhan98 suitable for partially irrigated ecosystem in Bangladesh 开发适合孟加拉国部分灌溉生态系统的气候智能型移栽 Aus 稻品种 BRRI dhan98 的数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-11-08 DOI: 10.1016/j.dib.2024.111092

Sanjoy K. Debsharma , Fahamida Akter , Md. Abu Syed , Mohammad Rafiqul Islam , Md. Rokebul Hasan , Md. Adil , Md. Ruhul Amin Sarker , Biswajit Karmakar , Mahmuda Khatun

This dataset provides an in-depth analysis of rice yield and grain quality attributes in four successive four years across 27 diverse environments in Bangladesh. The analysis emphasizes assessing the performance of studied genotypes (GEN), environments (ENV), and their interrelations (GEI). The research aim is to detect a stable and adaptive rice cultivar that not only displays high yield, and better grain quality but also has molecular data to know favorable alleles and biotic and abiotic stress-related traits. The combined ANOVA revealed significant GEN, ENV, and GEI effects on grain yield (p ≤ 0.001). Cultivar BR9011-67-4-1 produced the highest mean yield at 4.77 t/ha, whereas BRRI dhan48 had the lowest yield at 4.66 t/ha. In the C1E2 environment, BR9011-67-4-1 exhibited the highest yield at 6.06 t/ha. The broad-sense heritability was found at 0.481 and selection accuracy was accounted (0.985). AMMI analysis displayed significant effects on yield from ENV, GEN, and GEI (<0.0001), with the first two principal components elucidating 100 % of the variance. BR9011-67-4-1 was found the most stable cultivar based on AMMI stability analysis, with the highest AMMI-based stability parameter (ASTAB), AMMI stability value (ASV), and AMMI stability index (ASI). Based on the GGE biplot, BR9011-67-4-1 performed better in eleven environments. In the grain quality analysis, BR9011-67-4-1 had the highest amylose (27.9 %) and protein content (9.5 %) compared to BR26 and BRRI dhan48. Head rice yield positively correlated with milled rice breadth and thousand seed weight. Based on single nucleotide polymorphism (SNP) markers, BR9011-67-4-1 possessed 9 significant QTLs having grain qualities traits (amylose content, chalkiness, and grain number), abiotic stress (salt and cold tolerance, drought), and biotic stress (blast, BLB, BPH) compare to other cultivars. Finally, the genotype BR9011-67-4-1 has been released as BRRI dhan98 in the year of 2020 by 103th National Seed Board committee and its outcome highlight the importance of selecting suitable cultivar for the commercial rice production throughout Bangladesh and accounting for environmental conditions in rice breeding programs.

该数据集深入分析了孟加拉国 27 种不同环境下连续四年的水稻产量和谷物品质属性。分析的重点是评估所研究的基因型（GEN）、环境（ENV）及其相互关系（GEI）的表现。研究的目的是发现一种稳定的、适应性强的水稻栽培品种，这种品种不仅产量高、谷物品质好，而且还能通过分子数据了解有利等位基因以及与生物和非生物胁迫相关的性状。综合方差分析显示，GEN、ENV 和 GEI 对谷物产量有显著影响（p ≤ 0.001）。栽培品种 BR9011-67-4-1 的平均产量最高，为 4.77 吨/公顷，而 BRRI dhan48 的产量最低，为 4.66 吨/公顷。在 C1E2 环境中，BR9011-67-4-1 产量最高，为 6.06 吨/公顷。广义遗传力为 0.481，选择精确度为 0.985。AMMI分析显示，ENV、GEN和GEI对产量有显著影响（<0.0001），前两个主成分阐明了100%的方差。根据 AMMI 稳定性分析，BR9011-67-4-1 是最稳定的栽培品种，其基于 AMMI 的稳定性参数（ASTAB）、AMMI 稳定性值（ASV）和 AMMI 稳定性指数（ASI）均最高。根据 GGE 双图，BR9011-67-4-1 在 11 种环境中表现较好。在谷粒品质分析中，与 BR26 和 BRRI dhan48 相比，BR9011-67-4-1 的直链淀粉含量（27.9%）和蛋白质含量（9.5%）最高。头米产量与碾米宽度和千粒重呈正相关。根据单核苷酸多态性（SNP）标记，与其他品种相比，BR9011-67-4-1 在谷粒品质性状（直链淀粉含量、垩白度和粒数）、非生物胁迫（耐盐性和耐寒性、干旱）和生物胁迫（稻瘟病、白叶枯病、BPH）方面具有 9 个显著的 QTLs。最后，BR9011-67-4-1 基因型已于 2020 年由第 103 届国家种子委员会发布为 BRRI dhan98，其结果突显了为孟加拉国商业化水稻生产选择合适的栽培品种以及在水稻育种计划中考虑环境条件的重要性。

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引用次数: 0

RiGaD: An aerial dataset of rice seedlings for assessing germination rates and density RiGaD：用于评估发芽率和密度的水稻秧苗航空数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-11-06 DOI: 10.1016/j.dib.2024.111118

Trong Hieu Luu , Hoang-Long Cao , Quang Hieu Ngo , Thanh Tam Nguyen , Ilias El Makrini , Bram Vanderborght

The popularity of Unmanned Aerial Vehicles (UAVs) in agriculture makes data collection more affordable, facilitating the development of solutions to improve agricultural quality. We present a dataset of rice seedlings extracted from aerial images captured by a UAV under various environmental conditions. We focus on rice seedlings cultivated by the sowing method during their early growth stages because these stages are important to the establishment and survival as well as foundation for lifelong growth. We employed an adaptive thresholding method to isolate rice seedlings from the aerial images. We subsequently classified them into three categories based on their germination conditions: single rice seedings, clustered rice seed plants, and undefined objects. We obtained a total of 5364 labeled images of rice seedlings through data augmentation. This dataset serves as a resource for assessing germination rates and density using machine learning methods. The results derived from these assessments help farmers understand seedling growth and enable them to monitor the health and vigor of rice seedling during early growth stages.

无人驾驶飞行器（UAV）在农业领域的普及使数据收集变得更加经济实惠，有助于开发提高农业质量的解决方案。我们介绍了在各种环境条件下从无人机拍摄的航空图像中提取的水稻秧苗数据集。我们的研究重点是采用播种法培育的水稻秧苗的早期生长阶段，因为这些阶段对秧苗的成活和存活非常重要，也是秧苗终生生长的基础。我们采用自适应阈值法从航空图像中分离出水稻秧苗。随后，我们根据秧苗的发芽情况将其分为三类：单株水稻秧苗、丛生水稻秧苗和未定义对象。通过数据扩增，我们总共获得了 5364 张标注过的水稻秧苗图像。该数据集可作为使用机器学习方法评估发芽率和密度的资源。这些评估得出的结果有助于农民了解秧苗的生长情况，使他们能够在水稻秧苗的早期生长阶段监测其健康状况和活力。

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引用次数: 0

An extensive image dataset for deep learning-based classification of rice kernel varieties in Bangladesh 基于深度学习的孟加拉国水稻核心品种分类的广泛图像数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-11-06 DOI: 10.1016/j.dib.2024.111109

Md Tahsin, Md. Mafiul Hasan Matin, Mashrufa Khandaker, Redita Sultana Reemu, Mehrab Islam Arnab, Mohammad Rifat Ahmmad Rashid, Md Mostofa Kamal Rasel, Mohammad Manzurul Islam, Maheen Islam, Md. Sawkat Ali

This article introduces a comprehensive dataset developed in collaboration with the Bangladesh Institute of Nuclear Agriculture (BINA) and the Bangladesh Rice Research Institute (BRRI), featuring high-resolution images of 38 local rice varieties. Captured using advanced microscopic cameras, the dataset comprises 19,000 original images, enhanced through data augmentation techniques to include an additional 57,000 images, totaling 76,000 images. These techniques, which include transformations such as scaling, rotation, and lighting adjustments, enrich the dataset by simulating various environmental conditions, providing a broader perspective on each variety. The diverse array of rice strains such as BD33, BD30, BD39, among others, are meticulously detailed through their unique characteristics—color, size, and utility in agriculture—providing a rich resource for research. This augmented dataset not only enhances the understanding of rice diversity but also supports the development of innovative agricultural practices and breeding programs, offering a critical tool for researchers aiming to analyze and leverage rice genetic diversity effectively.

本文介绍了与孟加拉国核农业研究所（BINA）和孟加拉国水稻研究所（BRRI）合作开发的综合数据集，其中包含 38 个当地水稻品种的高分辨率图像。该数据集使用先进的显微照相机拍摄，包含 19,000 张原始图像，并通过数据增强技术对另外 57,000 张图像进行了增强，共计 76,000 张图像。这些技术包括缩放、旋转和光照调整等变换，通过模拟各种环境条件丰富了数据集，为每个品种提供了更广阔的视角。BD33、BD30、BD39 等各种水稻品系通过其独特的特征--颜色、大小和在农业中的用途--得到了细致入微的描述，为研究提供了丰富的资源。这一扩充数据集不仅增强了人们对水稻多样性的了解，还支持了创新农业实践和育种计划的发展，为旨在有效分析和利用水稻遗传多样性的研究人员提供了重要工具。

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引用次数: 0

Dataset on soil nematode abundance and composition from invaded and non-invaded grassland and forest ecosystems in Europe 欧洲已入侵和未入侵草地和森林生态系统土壤线虫丰度和组成数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-11-04 DOI: 10.1016/j.dib.2024.111098

Andrea Čerevková , Volodimir Sarabeev , Marek Renčo

The dataset presents comprehensive information on soil nematode genera distribution in ecosystems across Slovakia, Poland, Lithuania, and Russia. Data were collected from invaded plots by invasive plants and non-invaded plots from grasslands, deciduous forests, and coniferous forest ecosystems in diverse geographical regions. Invasive plant species included in this dataset are Asclepias syriaca, Fallopia japonica, Heracleum mantegazzianum, H. sosnowskyi, Impatiens parviflora and Solidago gigantea. The soil properties such as pH, moisture content, carbon, and nitrogen levels were recorded, providing comprehensive information on soil conditions. The data collection process involved standardized soil sampling techniques across all sites, ensuring consistency and comparability. The dataset offers valuable insights into soil nematode biodiversity dynamics in response to plant species invasions in European ecosystems. Nematode genera were classified according to feeding types and colonizer-persister class. Researchers interested in soil ecology, biodiversity conservation, and invasive species management can use this dataset for various purposes. Potential reuses include comparative analyses of nematode community composition, ecological modelling to predict invasive species impacts and assessments of ecosystem health and resilience.

该数据集全面介绍了斯洛伐克、波兰、立陶宛和俄罗斯生态系统中土壤线虫属的分布情况。数据收集自不同地理区域的入侵植物入侵地块和非入侵地块，包括草地、落叶林和针叶林生态系统。数据集中的入侵植物物种包括 Asclepias syriaca、Fallopia japonica、Heracleum mantegazzianum、H. sosnowskyi、Impatiens parviflora 和 Solidago gigantea。记录的土壤特性包括 pH 值、含水量、碳含量和氮含量，从而提供有关土壤条件的全面信息。数据收集过程涉及所有地点的标准化土壤取样技术，确保了一致性和可比性。该数据集为了解欧洲生态系统中植物物种入侵时土壤线虫的生物多样性动态提供了宝贵的信息。线虫属按照取食类型和定植者-传播者类别进行了分类。对土壤生态学、生物多样性保护和入侵物种管理感兴趣的研究人员可将该数据集用于多种用途。潜在的再利用包括线虫群落组成的比较分析、预测入侵物种影响的生态建模以及生态系统健康和恢复力评估。

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引用次数: 0

Abundance and composition data of microbiomes in agricultural biogas plants of Lower Saxony, Germany, with variation in organic substrates, process parameters and nutrients 德国下萨克森州农业沼气厂微生物群的丰度和组成数据，以及有机基质、工艺参数和营养物质的变化

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-11-04 DOI: 10.1016/j.dib.2024.111095

Sascha M.B. Krause , Rui Wang , Anja B. Dohrmann , Meike Walz , Achim Loewen , Christoph C. Tebbe

This article presents high-throughput DNA sequencing, quantitative PCR data of microbial communities, and process parameters as recovered from eight biogas plants (BPs) located in Lower Saxony, Germany. Samples were collected from both the main (MD) and secondary digesters (SD). Additionally, for 4 BPs, samples were also obtained from the residue digester storage (RDS). Different BPs employed various types of substrates originating from cattle manure, chicken manure, pig manure, or renewable resources. Information on physico-chemical process parameters and concentrations of macro- and micro-nutrients in the BPs is provided. Total DNA from all samples were extracted using a phenol-chloroform-based method. To determine the abundance of bacteria and archaea, their 16S rRNA genes were quantified by real-time PCR (qPCR), and to characterize their community composition, paired-end DNA sequence reads were generated from PCR amplicons with Illumina MiSeq. All statistical analyses were performed in R to explore the microbial diversity, abundance, and community structure among different BPs and digesters (MD, SD, RDS). The presence and distribution of the major bacterial and archaeal phyla indicated for each BP unique and diverse microbial communities with typically higher bacterial than archaeal abundances.

本文介绍了从德国下萨克森州的八家沼气厂（BPs）回收的高通量 DNA 测序、微生物群落定量 PCR 数据和工艺参数。样本采集自主沼气池（MD）和副沼气池（SD）。此外，有 4 家沼气厂还从残渣消化器存储区 (RDS) 采集了样品。不同的生物处理厂采用不同类型的基质，这些基质来自牛粪、鸡粪、猪粪或可再生资源。本报告提供了有关生物反应器的物理化学工艺参数以及宏量和微量营养素浓度的信息。所有样本的总 DNA 均采用苯酚-氯仿法提取。为了确定细菌和古细菌的丰度，采用实时 PCR（qPCR）技术对它们的 16S rRNA 基因进行了定量分析；为了描述它们的群落组成，采用 Illumina MiSeq 技术从 PCR 扩增子中生成了成对的 DNA 序列读数。所有统计分析均使用 R 语言进行，以探索不同生物处理剂和消化器（MD、SD、RDS）之间的微生物多样性、丰度和群落结构。主要细菌和古细菌门的存在和分布表明，每个生物处理厂都有独特而多样的微生物群落，细菌的丰度通常高于古细菌。

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引用次数: 0

High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forests 中欧温带阔叶林 350 万个性状和结构组合的高光谱查找表的高空间和光谱分辨率数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-11-03 DOI: 10.1016/j.dib.2024.111105

Tomáš Hanousek , Terézia Slanináková , Tomáš Rebok , Růžena Janoutová

Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests.

The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT.

The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.

从遥感数据中准确检索森林功能特征对于监测森林健康和生产力至关重要。要利用反演方法达到足够的精确度，必须拥有具有代表性的模拟或测量光谱特性数据库以及相应的森林特征。然而，现有的数据集通常范围有限，涵盖特定地点和时间，结构简化。这种局限性阻碍了用于性状预测的通用机器学习模型的开发。为了解决这个问题，我们提出了一个针对中欧温带阔叶林的高光谱查找表（LUT）综合高分辨率数据集。该数据集包括 350 万个森林场景的叶片生化和冠层结构特征的独特组合以及各种太阳几何形状。光谱数据涵盖 450 纳米到 2300 纳米的波长，分辨率为 2 纳米。数据集分为两个文件：一个捕捉所有场景像素的平均反射率，另一个只关注阳光下的叶片像素。LUT 使用离散各向异性辐射传输模型 5.10.0 版生成。虚拟森林场景基于欧洲山毛榉树地面激光扫描得出的三维树形，并根据不同的叶面积指数值和结构配置进行了调整，以模拟森林的自然变化。该数据集可用于训练随机森林和支持向量机等机器学习模型，以预测森林功能特征并协助校准遥感算法。该数据集的最大优势是光谱和空间分辨率高，同时具有大量不同的性状组合，可适应不同的时间、地点以及高光谱和多光谱传感器，并可支持即将到来的高光谱卫星任务。欧空局哥白尼环境高光谱成像任务（CHIME）和美国国家航空航天局（NASA）地表生物学和地质学（SBG）未来的卫星任务可利用该数据集开发其产品处理器，用于监测森林特征。

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引用次数: 0

A novel automated cloud-based image datasets for high throughput phenotyping in weed classification 用于杂草分类中高通量表型分析的新型自动云图像数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-11-01 DOI: 10.1016/j.dib.2024.111097

Sunil G C , Cengiz Koparan , Arjun Upadhyay , Mohammed Raju Ahmed , Yu Zhang , Kirk Howatt , Xin Sun

Deep learning-based weed detection data management involves data acquisition, data labeling, model development, and model evaluation phases. Out of these data management phases, data acquisition and data labeling are labor-intensive and time-consuming steps for building robust models. In addition, low temporal variation of crop and weed in the datasets is one of the limiting factors for effective weed detection model development. This article describes the cloud-based automatic data acquisition system (CADAS) to capture the weed and crop images in fixed time intervals to take plant growth stages into account for weed identification. The CADAS was developed by integrating fifteen digital cameras in the visible spectrum with gphoto2 libraries, external storage, cloud storage, and a computer with Linux operating system. Dataset from CADAS system contain six weed species and eight crop species for weed and crop detection. A dataset of 2000 images per weed and crop species was publicly released. Raw RGB images underwent a cropping process guided by bounding box annotations to generate individual JPG images for crop and weed instances. In addition to cropped image 200 raw images with label files were released publicly. This dataset hold potential for investigating challenges in deep learning-based weed and crop detection in agricultural settings. Additionally, this data could be used by researcher along with field data to boost the model performance by reducing data imbalance problem.

基于深度学习的杂草检测数据管理涉及数据采集、数据标注、模型开发和模型评估等阶段。在这些数据管理阶段中，数据采集和数据标注是建立稳健模型的劳动密集型耗时步骤。此外，数据集中作物和杂草的时间变化较小，也是限制有效开发杂草检测模型的因素之一。本文介绍了基于云的自动数据采集系统（CADAS），该系统以固定的时间间隔采集杂草和作物图像，并将植物生长阶段纳入杂草识别的考虑范围。CADAS 是通过将 15 台可见光谱数码相机与 gphoto2 库、外部存储、云存储和装有 Linux 操作系统的计算机集成而开发的。CADAS 系统的数据集包含用于杂草和作物检测的 6 种杂草和 8 种作物。每个杂草和作物物种包含 2000 张图像的数据集已公开发布。原始 RGB 图像在边界框注释的引导下经过裁剪处理，为作物和杂草实例生成单独的 JPG 图像。除裁剪图像外，还公开发布了 200 张带标签文件的原始图像。该数据集可用于研究农业环境中基于深度学习的杂草和作物检测所面临的挑战。此外，研究人员还可以将这些数据与田间数据一起使用，通过减少数据不平衡问题来提高模型性能。

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引用次数: 0

Data set for estimating combining abilities for yield and quality attributes in summer tomato using line by tester analysis in Bangladesh 利用逐行测试分析法估算孟加拉国夏季番茄产量和质量属性组合能力的数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-10-31 DOI: 10.1016/j.dib.2024.111063

Mohammad Matin Akand , Mohammed Abu Taher Masud , Md. Azizul Hoque , Mohammad Mostafa Kamal , Mohammad Rezaul Karim , Bahauddin Ahmed

This article provides a dataset for line × tester analysis in the F1 generation of summer tomatoes using open-source R statistical software and the ‘agricolae’ package. The dataset includes seven inbred lines as female parents (L) and two testers as male parents (T) with diverse genetic bases and heat tolerance qualities. Fourteen cross combinations were produced through L × T (7 × 2) mating design, involving hybridization between lines (f) and testers (m) in a one-to-one fashion. To assess the heterosis of the crosses, all parents (both lines and testers) were included along with the crosses and evaluated in the same experimental field for 16 traits using a randomized complete block design (RCBD) with two replications. The line × tester analysis estimates the ANOVA, including parents, combining ability, genetic components, and the contribution of parental lines to genetic variation in the hybrids. This dataset is valuable for breeders in subtropical countries to develop efficient breeding strategies for hybrid summer tomato varieties.

本文利用开源 R 统计软件和 "agricolae "软件包提供了一个数据集，用于分析夏季番茄 F1 代中的品系×测试者。数据集包括作为雌性亲本（L）的七个近交系和作为雄性亲本（T）的两个测试者，它们具有不同的遗传基础和耐热性。通过 L × T（7 × 2）交配设计产生了 14 个杂交组合，其中包括品系（f）和测试者（m）之间一对一的杂交。为了评估杂交组合的异交性，所有亲本（包括品系和测试者）都被纳入杂交组合，并在同一试验田中采用随机完全区组设计（RCBD）对 16 个性状进行了评估。品系×测试者分析估计了方差分析，包括亲本、结合能力、遗传成分以及亲本品系对杂交种遗传变异的贡献。该数据集对亚热带国家的育种者制定夏季杂交番茄品种的高效育种策略很有价值。

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引用次数: 0

A dataset examining technical factors on fixed white blood cell single-cell RNA-seq 研究固定白细胞单细胞 RNA-seq 技术因素的数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2024-10-31 DOI: 10.1016/j.dib.2024.111096

Daniel V Brown , Agnieszka Swierczak , Yue You , Yupei You , Daniela Amann-Zalcenstein , Peter Hickey , Arthur Hsu , Matthew E. Ritchie , Monther Alhamdoosh , Judith Field , Rory Bowden

Single-cell RNA sequencing (scRNA-seq) is a powerful technology that enables the measurement of gene expression in individual cells. Such precision provides insights into cellular heterogeneity that bulk methods might overlook. Fragile cells, in particular neutrophils, have posed significant challenges for scRNA-Seq due to their ex vivo fragility, high RNase content and consequent loss during cryopreservation. The introduction of fixed scRNA-Seq methodology offers a promising solution to these challenges. We evaluated the performance of two different commercial platforms on red blood cell-depleted whole blood cells: 10x Genomics Flex v1 and Honeycomb HIVE v1.

These data are publicly available from the Gene Expression Omnibus database (accession number GSE266615).

Further insights could be gained by correcting batch and technical effects introduced by storage time after fixation and cell numbers fixed. These data may be used to examine how reflective the transcriptome of neutrophils are of the native environment.

单细胞 RNA 测序（scRNA-seq）是一项功能强大的技术，可测量单个细胞的基因表达。这种精确性使人们能够深入了解大量方法可能忽略的细胞异质性。脆性细胞，特别是中性粒细胞，由于其体内脆性、高 RNase 含量以及在冷冻保存过程中的损失，给 scRNA 测序带来了巨大挑战。固定 scRNA-Seq 方法的引入为解决这些难题提供了一个前景广阔的解决方案。我们评估了两种不同商业平台在去红细胞全血细胞上的性能：10x Genomics Flex v1 和 Honeycomb HIVE v1。这些数据可从基因表达总库数据库（登录号 GSE266615）公开获取。这些数据可用于研究中性粒细胞转录组对原生环境的反映程度。

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