Data in Brief最新文献_第5页

An open image dataset of Indonesian soybean seed varieties (Anjasmoro, Grobogan, DEGA-1) for agricultural research and machine learning applications 印度尼西亚大豆种子品种（Anjasmoro, Grobogan, DEGA-1）的开放图像数据集，用于农业研究和机器学习应用

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-02-03 DOI: 10.1016/j.dib.2026.112524

Diana Sofia Hanafiah , Rahmatika Alfi , Anggria Lestami , Fanindia Purnamasari , Rossy Nurhasanah , Muhammad Ariyo Syahraza , Muhammad Azis Saputra , Usman Ismail Pane , Steven Manurung , Keisya , Yunus Tio Buntoro , Josua Peter Corda , Gali Rakasiwi

Soybean (Glycine max L.) performs an important position as a main resource of protein in Indonesia. Its quality and productivity can be assessed based on the characteristics of its seed. Accordingly, the identification process through the observation of soybean seed traits is a crucial step in plant breeding and quality assurance. Manual approaches rely on manual observation, which is subjective, prone to human error and time-consuming. With the improvement of artificial intelligence, automated seed identification has appeared as a potential solution. However, progress is constrained by the lack of open and standardized image datasets, especially for locally bred varieties in developing countries. To address this gap, we propose an open image dataset of Indonesian soybean seeds from three widely cultivated and plant-bred varieties: Anjasmoro, Grobogan, and DEGA-1. The dataset consists of high-resolution seed images captured with an Epson L360 flatbed scanner, with the optical resolution fixed at 800 dots per inch, yielding images of 6800 × 9359 pixels. All raw images are saved in JPG format. No manually segmentation masks are released in this version, instead of using Deeplab V3+ with MobileNet as backbone to enable the automated seed image segmentation. The curated dataset is intended to support a broad range of applications, including computer vision tasks such as image classification and segmentation, as well as research in plant breeding, seed quality assessment, and agricultural informatics. By providing a standardized and publicly accessible resource, this dataset contributes to the advancement of interdisciplinary studies at the intersection of agriculture and artificial intelligence.

大豆（Glycine max L.）在印度尼西亚作为蛋白质的主要来源占有重要地位。根据其种子的特性可以评价其质量和产量。因此，通过观察大豆种子性状进行鉴定是植物育种和质量保证的关键步骤。人工方法依赖于人工观察，这是主观的，容易出现人为错误并且耗时。随着人工智能的提高，自动种子识别已经成为一种潜在的解决方案。然而，由于缺乏开放和标准化的图像数据集，特别是发展中国家本地育种品种的图像数据集，进展受到限制。为了解决这一差距，我们提出了一个开放的印度尼西亚大豆种子图像数据集，这些种子来自三个广泛种植和植物育种的品种：Anjasmoro、Grobogan和DEGA-1。数据集由Epson L360平板扫描仪拍摄的高分辨率种子图像组成，光学分辨率固定为800点/英寸，生成6800 × 9359像素的图像。所有原始图像都以JPG格式保存。在这个版本中没有发布手动分割掩码，而是使用Deeplab V3+与MobileNet作为主干来实现自动种子图像分割。整理的数据集旨在支持广泛的应用，包括计算机视觉任务，如图像分类和分割，以及植物育种，种子质量评估和农业信息学研究。通过提供标准化和可公开访问的资源，该数据集有助于推进农业和人工智能交叉领域的跨学科研究。

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引用次数: 0

The complete genome sequencing data of Priestia aryabhattai SPCL1 isolated from a heavy metal leachate-contaminated soil in Queretaro, México 从墨西哥克雷塔罗市重金属渗滤液污染土壤中分离的Priestia aryabhattai SPCL1全基因组测序数据

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-02-07 DOI: 10.1016/j.dib.2026.112561

Mario Eduardo Clemente Albores , María De Los Ángeles Hernández Tépach , Paola Itzel Herrera de la Torre , Mayra Paola Mena Navarro , María Carlota García Gutiérrez , Karla Isabel Lira De León , David Gustavo García Gutiérrez , Aldo Amaro Reyes , Miguel Angel Ramos López , José Alberto Rodríguez Morales , Erika Álvarez Hidalgo , Sergio de Jesús Romero Gómez , Juan Campos Guillén

We are providing the genome sequence of Priestia aryabhattai SPCL1, a bacterial strain isolated from a heavy metal leachate-contaminated soil in Querétaro, México. The Illumina NovaSeq platform was used to sequence the whole genome and the sequencing data obtained, including assembly and annotation, were analyzed on the BV-BRC platform. The genome, comprising 41 contigs and approximately 5.6 million base pairs with a GC content of 37.58 mol % and 6131 protein-coding sequences. In addition, 6 contigs of 146,177 bp (36.77 mol % G + C), 126,627 bp (33.27 mol % G + C), 16,881 bp (34.13 mol % G + C), 9835 bp (34.67 mol % G + C), 7402 bp (36.54 mol % G + C) and 4590 bp (35.38 mol % G + C) were assembled as plasmids. This analysis of genomic data represents a valuable resource for increasing knowledge of this bacterial specie and for possible applications in its biological functions. The genome data was deposited at National Center for Biotechnology Information (NCBI) under accession number Bioproject ID PRJNA1377581, Bio Sample ID SAMN53794006 and genome accession number ID JBSVDB000000000.

我们正在提供Priestia aryabhattai SPCL1的基因组序列，这是一种从墨西哥奎尔凯萨罗省重金属渗滤液污染的土壤中分离出来的细菌菌株。采用Illumina NovaSeq平台对全基因组进行测序，获得的测序数据在BV-BRC平台上进行分析，包括组装和注释。该基因组包含41个contigs，约560万个碱基对，GC含量为37.58 mol %，有6131个蛋白质编码序列。另外，共组装了146,177 bp （36.77 mol % G + C）、126,627 bp （33.27 mol % G + C）、16,881 bp （34.13 mol % G + C）、9835 bp （34.67 mol % G + C）、7402 bp （36.54 mol % G + C）和4590 bp (35.38 mol % G + C) 6个质粒。这种基因组数据的分析为增加对这种细菌物种的认识和在其生物学功能方面的可能应用提供了宝贵的资源。基因组数据保存在国家生物技术信息中心（NCBI），登录号为Bioproject ID PRJNA1377581， Bio Sample ID SAMN53794006，基因组登录号为JBSVDB000000000。

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引用次数: 0

Dataset reporting the differential effect of HNRNPA1 isoforms on alternative splicing 数据集报告了HNRNPA1亚型对选择性剪接的差异影响

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.dib.2026.112544

Jade-Emmanuelle Deshaies , Valérie Triassi , Martine Tétreault , Christine Vande Velde

Heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) encodes two main protein coding variants: hnRNP A1 and hnRNP A1B. The isoforms differ by the exclusion or inclusion of exon 8 (sometimes referred to as exon 7B), which extends the length of the intrinsically disordered region (IDR). HnRNP A1 is implicated in most major steps of nascent RNA transcript processing, with RNA splicing being the most studied function. While hnRNP A1 has been studied extensively, little is known about the relevance of the longer isoform, hnRNP A1B. In fact, with respect to alternative splicing, only two groups have reported a functional analysis of both isoforms, revealing that both isoforms modulate alternative splicing, albeit with different efficiencies. To better understand the contribution of each isoform on alternative splicing, we analyzed the transcriptomes of mouse erythroleukemia cells either lacking HNRNPA1 (CB3) or uniquely expressing one isoform [hnRNP A1 (CB3 A1) or hnRNP A1B (CB3 A1B)] via stable constitutive expression of murine cDNAs. Our data indicate that differential isoform expression modulates the splicing of both shared and isoform-specific gene sets. These genes are involved in a wide variety of molecular functions and biological processes. Finally, and intriguingly, analysis of the genes with the largest differences in inclusion levels revealed enrichment for genes implicated in several neurodegenerative and neurodevelopmental diseases, as well as intellectual disability, myopathy and cancer.

异质核核糖核蛋白A1 （HNRNPA1）编码两种主要的蛋白质编码变体：hnRNP A1和hnRNP A1B。同种异构体的不同之处在于排除或包含外显子8（有时称为外显子7B），这延长了内在无序区（IDR）的长度。HnRNP A1参与了新生RNA转录加工的大多数主要步骤，其中RNA剪接是研究最多的功能。虽然hnRNP A1已被广泛研究，但人们对其较长的同工异构体hnRNP A1B的相关性知之甚少。事实上，关于选择性剪接，只有两个研究小组报道了两种同工异构体的功能分析，揭示了两种同工异构体调节选择性剪接，尽管效率不同。为了更好地了解每种异构体对选择性剪接的贡献，我们分析了缺乏HNRNPA1 （CB3）或通过小鼠cdna的稳定组成表达唯一表达一种异构体[hnRNP A1 （CB3 A1）或hnRNP A1B (CB3 A1B)]的小鼠红白血病细胞的转录组。我们的数据表明，差异异构体表达调节了共享和异构体特异性基因集的剪接。这些基因参与了各种各样的分子功能和生物过程。最后，有趣的是，对包含水平差异最大的基因的分析显示，与几种神经退行性和神经发育疾病、智力残疾、肌病和癌症有关的基因富集。

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引用次数: 0

A dataset for human-written and AI-generated code source classification 一个用于人类编写和人工智能生成的代码源分类的数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1016/j.dib.2026.112527

Ghizlane Boukili, Said EL Garouani, Jamal Riffi

The rapid rise of AI code generation tools has created significant challenges for computer science educators in verifying the authenticity of students’ code writing. While generic AI detection tools exist, they often struggle to accurately identify AI-generated code because of the unique patterns and structures of programming languages. Research in this area requires data to develop effective systems for detecting AI code. We introduce a specialized dataset designed to support the creation of domain-specific detection tools to fill this gap. The dataset comprises 10,000 annotated code samples, consisting of 5000 human-written and 5000 AI-generated code samples, across Python, Java, C, and C++. The human-written samples were collected from a public repository, while the AI-generated samples were produced using ChatGPT’s API with varied prompts. Each sample is labeled with its origin, whether human or AI, enabling the robust training of machine learning and deep learning models for code source discrimination. The dataset and experiment code are publicly available to support further research in AI-generated code detection.

人工智能代码生成工具的迅速崛起，给计算机科学教育者在验证学生代码编写的真实性方面带来了重大挑战。虽然存在通用的人工智能检测工具，但由于编程语言的独特模式和结构，它们往往难以准确识别人工智能生成的代码。这一领域的研究需要数据来开发有效的检测人工智能代码的系统。我们引入了一个专门的数据集，旨在支持创建特定于领域的检测工具来填补这一空白。该数据集包括10,000个带注释的代码样本，包括5000个人工编写和5000个人工智能生成的代码样本，支持Python， Java， C和c++。人类编写的样本是从公共存储库中收集的，而人工智能生成的样本是使用ChatGPT的API生成的，并带有各种提示。每个样本都标有其来源，无论是人类还是人工智能，都可以对机器学习和深度学习模型进行强大的训练，以进行代码源识别。数据集和实验代码是公开的，以支持人工智能生成的代码检测的进一步研究。

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引用次数: 0

A 3D point cloud dataset of Jining Qing Goats for segmentation analysis and body size measurement 济宁青山羊三维点云数据集的分割分析与体型测量

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-01-20 DOI: 10.1016/j.dib.2026.112496

Kai Zhang, Qin Ma, Yichen Liu, Xiaochen Shi

The rapid advancement of intelligent livestock farming and precision breeding has underscored the importance of non-contact body measurement and weight estimation. Based on 3D reconstruction, these techniques represent a critical pathway for the digital transformation of animal husbandry. However, there are no publicly available 3D point cloud datasets specific to Jining Qing Goats, particularly systematic data covering key developmental stages. To bridge this gap, we present a comprehensive dataset comprising multi-view 3D point clouds and standardized morphometric records of Jining Qing Goats. The dataset spans multiple age groups and emphasizes critical phases such as juvenile, growing, mature, and reproductive stages, thereby capturing a holistic representation of the breed’s life cycle. During data acquisition, two Microsoft Kinect DK depth cameras were positioned bilaterally to capture RGB and depth images simultaneously under relatively static conditions. Multi-view point clouds were registered using the Iterative Closest Point (ICP) algorithm, with the floor plane serving as a unified reference to align all scans within a global coordinate system. In parallel, manual measurements of six key morphometric traits, including body length, withers height, shoulder width, abdominal width, heart girth and hip width, were collected as validation references. The dataset consists of raw RGB images, depth maps, point cloud files, camera calibration parameters, and manually annotated measurement records, all of which are openly accessible. This resource supports a wide range of computer vision tasks such as livestock 3D reconstruction, automated morphometric measurement, and weight estimation, thereby facilitating digital transformation, intelligent management, and sustainable development in modern livestock farming.

畜禽智能养殖和精准养殖的快速发展凸显了非接触体测量和体重估算的重要性。基于三维重建，这些技术代表了畜牧业数字化转型的关键途径。然而，目前还没有针对济宁青山羊的公开的三维点云数据集，特别是覆盖关键发育阶段的系统数据。为了弥补这一差距，我们提出了一个综合数据集，包括济宁青山羊的多视图三维点云和标准化形态测量记录。该数据集跨越多个年龄组，并强调关键阶段，如幼年、生长、成熟和繁殖阶段，从而捕捉到品种生命周期的整体表现。在数据采集过程中，两台Microsoft Kinect DK深度摄像头被放置在两侧，在相对静态的条件下同时捕获RGB和深度图像。使用迭代最近点（ICP）算法注册多视图点云，地板平面作为统一参考，在全局坐标系内对齐所有扫描。同时，收集6个关键形态特征的人工测量值，包括体长、肩高、肩宽、腹宽、胸围和臀宽，作为验证参考。该数据集由原始RGB图像、深度图、点云文件、相机校准参数和手动注释的测量记录组成，所有这些都是开放访问的。该资源支持广泛的计算机视觉任务，如牲畜3D重建，自动形态测量和重量估计，从而促进现代畜牧业的数字化转型，智能管理和可持续发展。

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引用次数: 0

ThermVision-DB: A synthetic LWIR thermal face dataset for privacy-preserving thermal vision research ThermVision-DB：用于隐私保护热视觉研究的合成LWIR热人脸数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.dib.2026.112506

Muhammad Ali Farooq, Waseem Shariff, Peter Corcoran

ThermVision-DB presents a synthetic long-wave infrared (LWIR) facial dataset designed to support research in privacy-preserving vision, thermal perception, and multimodal facial analysis. The dataset builds upon generative diffusion models to create photorealistic thermal facial images and video sequences capturing controlled variations in facial expression and head pose. Each synthetic identity is generated using text-to-image conditioning followed by video retargeting module, enabling precise control over pose angles, expression intensity, and frame-to-frame consistency. The dataset includes a diverse set of synthetic adult identities of both male and female genders with multiple facial expressions - such as neutral, smile, frown, and surprise and head-pose rotations spanning yaw, pitch, and roll. Data are provided in both image and video formats, accompanied by face localization annotations, landmark detections and identity labels. To ensure reusability and scalability, all samples are generated through a standardized pipeline using open-source models, allowing researchers to easily expand the dataset with additional synthetic identities while maintaining consistent thermal appearance and scene illumination. The synthetic generation process avoids the use of any personally identifiable visual data, ensuring compliance with FAIR and GDPR principles.

ThermVision-DB is intended for use in developing and benchmarking algorithms for facial detection, landmark localization, expression recognition, and head-pose estimation in the thermal domain. It also provides a foundation for research in synthetic-to-real transfer learning, privacy-safe biometric analysis, and cross-spectrum data fusion. The dataset is released for open research purposes under a non-commercial license, with full documentation and metadata available to facilitate reproducibility and integration with existing thermal vision benchmarks.

ThermVision-DB提供了一个合成长波红外（LWIR）面部数据集，旨在支持隐私保护视觉、热感知和多模态面部分析的研究。该数据集建立在生成扩散模型的基础上，以创建逼真的热面部图像和视频序列，捕获面部表情和头部姿势的受控变化。每个合成身份都是通过文本到图像的调节和视频重定向模块生成的，可以精确控制姿态角度、表情强度和帧与帧之间的一致性。该数据集包括一组不同的合成成人身份，包括男性和女性性别，具有多种面部表情，如中性、微笑、皱眉、惊讶和头部姿势旋转，跨越偏斜、俯仰和翻滚。数据以图像和视频两种格式提供，并附有人脸定位注释、地标检测和身份标签。为了确保可重用性和可扩展性，所有样本都是通过使用开源模型的标准化管道生成的，这使得研究人员可以轻松地使用额外的合成身份扩展数据集，同时保持一致的热外观和场景照明。合成生成过程避免使用任何个人可识别的视觉数据，确保符合FAIR和GDPR原则。ThermVision-DB旨在用于开发和基准算法，用于热域的面部检测，地标定位，表情识别和头姿估计。它还为合成到真实的迁移学习、隐私安全生物特征分析和跨光谱数据融合的研究提供了基础。该数据集在非商业许可下发布，用于开放研究目的，提供完整的文档和元数据，以促进可重复性和与现有热视觉基准的集成。

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引用次数: 0

Gut microbiomes of wild and domesticated mammals and birds in Slovenia, Europe: 16S rRNA sequencing data 欧洲斯洛文尼亚野生和家养哺乳动物和鸟类的肠道微生物组：16S rRNA测序数据

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-02-09 DOI: 10.1016/j.dib.2026.112564

Zlender Tanja , Rupnik Maja

From a One Health perspective, the gut microbiota of animals acts as a major driver of microbial exchange between animals and the environment. Animals continuously release gut microbes into their surroundings, shaping environmental and human microbial communities and potentially dispersing pathogens. Characterizing gut microbiota across diverse animal hosts is therefore critical for understanding the patterns of microbial spread through ecosystems and their impact on animal, human and environmental health.

Here, we introduce a large, taxonomically diverse dataset of fecal microbiomes from 715 individual animals representing over 50 mammalian and avian species. We collected samples from both wild and domestic animals with an emphasis on capturing microbial diversity across a wide range of taxa and ecological contexts. The samples were subjected to 16S rRNA gene sequencing, targeting the V3–V4 hypervariable region. Bioinformatic analysis was performed using Usearch to generate zero-radius operational taxonomic units (ZOTUs).

This dataset was generated primarily for the development of microbial source tracking (MST) assays used for identifying the sources of fecal pollution in contaminated water. However, it provides a valuable resource for broader microbiome research. It enables comparative studies across host species, trophic guilds, and environmental contexts such as domestication.

从“同一个健康”的角度来看，动物的肠道微生物群是动物与环境之间微生物交换的主要驱动力。动物不断地将肠道微生物释放到周围环境中，形成环境和人类微生物群落，并可能传播病原体。因此，表征不同动物宿主的肠道微生物群对于理解微生物在生态系统中的传播模式及其对动物、人类和环境健康的影响至关重要。在这里，我们介绍了一个大型的、分类多样化的粪便微生物组数据集，来自715只动物，代表了50多种哺乳动物和鸟类。我们收集了野生和家养动物的样本，重点是在广泛的分类群和生态环境中捕捉微生物多样性。对样品进行16S rRNA基因测序，靶向V3-V4高变区。利用ussearch进行生物信息学分析，生成零半径操作分类单位（ZOTUs）。该数据集主要用于开发微生物源追踪（MST）分析，用于识别受污染水中的粪便污染源。然而，它为更广泛的微生物组研究提供了宝贵的资源。它使跨宿主物种、营养行会和环境背景（如驯化）的比较研究成为可能。

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引用次数: 0

Gonadal transcriptome data of fast and slow growth of Oreochromis niloticus 尼罗鱼快速和缓慢生长的性腺转录组数据

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.dib.2026.112525

Irmawati Irmawati , Dian Novita Sari , Andi Haerul , Alimuddin Alimuddin , Ade Yamindago , Rinto

Oreochromis niloticus, commonly known as Nile tilapia, is an economically significant freshwater fish widely cultured for consumption. Tilapia is one of the most important aquaculture commodities worldwide due to its high demand; therefore, it has been widely cultivated. Here we focus on two strains of Nile tilapia: the K-strain, characterized by its rapid growth, and the N-strain, known for its slower growth. To enrich the information on the genes involved in growth regulation, RNA sequencing was performed, and transcriptomic datasets were generated from the gonads of both strains. The raw and transcriptomic data have been deposited in the NCBI database, with BioProject Number: PRJNA1354995. These data will provide an essential source of information to enrich the genome annotation of this species, especially in understanding the mechanism underlying the regulation of genes in fast- and slow-growth tilapia.

尼罗罗非鱼（Oreochromis niloticus），俗称尼罗罗非鱼，是一种经济意义重大的淡水鱼，广泛养殖供消费。罗非鱼需求量大，是世界上最重要的水产养殖商品之一；因此，它被广泛种植。在这里，我们重点关注两种尼罗罗非鱼：以其快速生长为特征的k -株和以其缓慢生长而闻名的n -株。为了丰富与生长调控有关的基因信息，我们对这两株菌株的性腺进行了RNA测序，并生成了转录组数据集。原始数据和转录组学数据已存入NCBI数据库，生物项目编号：PRJNA1354995。这些数据将为丰富该物种的基因组注释提供重要的信息来源，特别是在了解快速和缓慢生长罗非鱼基因调控的机制方面。

引用次数: 0

Experimental dataset of the reverse water-gas shift reaction in a fixed-bed reactor setup under varying reactor conditions 不同反应器条件下固定床反应器中逆水气转换反应的实验数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-01-20 DOI: 10.1016/j.dib.2026.112485

Enzo Komatz , Marion Andritz , Christoph Markowitsch

This data article presents a dataset of miniplant-scale reverse water-gas shift (rWGS) experiments conducted in a heated fixed-bed reactor under systematically varied operating conditions. The dataset contains processed measurements including reactor temperature, molar fractions of CO₂, CO, H₂, CH₄, and derived quantities such as CO₂ conversion and CO selectivity. The experiments cover a wide parameter space, including gas hourly space velocities of 8000, 14,000 and 20,000 h^-1 with temperatures between 550 and 950 °C (increment of 50 K), and H₂:CO₂ feed ratios of 2:1, 2.5:1 and 3:1.

The dataset presents the steady-state values and links to the reproductible data processing step, based on a prior study, enabling Fairness of all steps from the initial measurements to the final processed variables. The processing workflow includes calibration of gas analysis signals, smoothing, dry-gas calculation, and uncertainty estimation.

These data provide value for validating mechanistic kinetic models, benchmarking computational fluid dynamics (CFD) reactor simulations, training machine learning models including physics-informed machine learning frameworks, and supporting thermodynamic model assessments. All raw and processed data are made publicly available in a long-term repository, ensuring FAIR access and enabling reuse by the scientific community.

本文介绍了在加热固定床反应器中在系统变化的操作条件下进行的小型工厂规模逆水气转换（rWGS）实验数据集。该数据集包含处理过的测量数据，包括反应器温度，CO2， CO, H2， CH4的摩尔分数，以及CO2转化率和CO选择性等衍生量。实验参数范围广，气体每小时空速为8000、14000和20000 h-1，温度为550 ~ 950℃（增量50 K）， H2:CO2进料比为2:1、2.5:1和3:1。该数据集基于先前的研究，呈现了稳态值并链接到可重复的数据处理步骤，从而实现了从初始测量到最终处理变量的所有步骤的公平性。处理流程包括气体分析信号的校准、平滑、干气计算和不确定度估计。这些数据为验证机械动力学模型、计算流体动力学（CFD）反应堆模拟基准、训练机器学习模型（包括物理知识的机器学习框架）以及支持热力学模型评估提供了价值。所有原始和处理过的数据都在一个长期存储库中公开提供，确保公平访问并使科学界能够重复使用。

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引用次数: 0

Corn seed dataset based on hyperspectral and RGB images 基于高光谱和RGB图像的玉米种子数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.dib.2026.112455

Chao LI , Chen Zhang , Wenbo Zhang , Chengzhen LV , Yaqiang Li , Yufen Wang

This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400–1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20–25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.

本研究采用HY-6010-S高光谱成像系统，覆盖400-1000 nm光谱范围，结合RGB工业相机获取多模态数据。该数据集模拟了受控实验室条件下玉米种子的表型分析情景，环境温度保持在20-25℃。采用12个不同的玉米品种进行了综合试验。每个品种大约收集了200个种子样本，总样本量约为2400个，每个样本都进行了高光谱和RGB图像采集。预处理步骤包括降噪、背景去除、波段选择和模态对齐。为了保证实验数据的准确性和可靠性，使用HHIT软件和Python进行数据处理。该数据集在种子品种分类、表型分析、精准农业和机器学习应用中发挥着重要作用。

引用次数: 0