Data in Brief最新文献_第8页

The Bangladesh road traffic sign dataset in real-world images for traffic sign recognition 用于交通标志识别的真实世界图像中的孟加拉国道路交通标志数据集

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Data in Brief Pub Date : 2025-03-27 DOI: 10.1016/j.dib.2025.111523

Md. Ariful Islam, Dewan Md. Farid

引用次数: 0

Draft genome sequence data on Bacillus safensis FB03 isolated from the rhizosphere soil of leguminous plant in Bangladesh 从孟加拉国豆科植物根际土壤分离的萨福芽孢杆菌FB03基因组序列数据草稿

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Data in Brief Pub Date : 2025-03-27 DOI: 10.1016/j.dib.2025.111527

Farhana Boby , Dr. Md Nurul Huda Bhuiyan , Dr. Md. Salim Khan , Md. Mashud Parvez

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RGB and RGNIR image dataset for machine learning in plastic waste detection 用于塑料垃圾检测机器学习的 RGB 和 RGNIR 图像数据集

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Data in Brief Pub Date : 2025-03-27 DOI: 10.1016/j.dib.2025.111524

Owen Tamin , Ervin Gubin Moung , Jamal Ahmad Dargham , Samsul Ariffin Abdul Karim , Ashraf Osman Ibrahim , Nada Adam , Hadia Abdelgader Osman

{"title":"RGB and RGNIR image dataset for machine learning in plastic waste detection","authors":"Owen Tamin , Ervin Gubin Moung , Jamal Ahmad Dargham , Samsul Ariffin Abdul Karim , Ashraf Osman Ibrahim , Nada Adam , Hadia Abdelgader Osman","doi":"10.1016/j.dib.2025.111524","DOIUrl":"10.1016/j.dib.2025.111524","url":null,"abstract":"<div><div>The increasing volume of plastic waste is an environmental issue that demands effective sorting methods for different types of plastic. While spectral imaging offers a promising solution, it has several drawbacks, such as complexity, high cost, and limited spatial resolution. Machine learning has emerged as a potential solution for plastic waste due to its ability to analyse and interpret large volumes of data using algorithms. However, developing an efficient machine learning model requires a comprehensive dataset with information on the size, shape, colour, texture, and other features of plastic waste. Moreover, incorporating near-infrared (NIR) spectral data into machine learning models can reveal crucial information about plastic waste composition and structure that remains invisible in standard RGB images. Despite this potential, no publicly available dataset currently combines RGB with NIR spectral information for plastic waste detection. To address this research gap, we introduce a comprehensive dataset of plastic waste images captured onshore using both standard RGB and RGNIR (red, green, near-infrared) channels. Each of the two-colour space datasets include 405 images that were taken along riverbanks and beaches. Both datasets underwent further pre-processing to ensure proper labelling and annotations to prepare them for training machine learning models. In total, there are 1,344 plastic waste objects that have been annotated. The proposed dataset offers a unique resource for researchers to train machine learning models for plastic waste detection. While there are existing datasets on plastic waste, the proposed dataset aims to set itself apart by offering a more comprehensive dataset with unique spectral information in the near-infrared region. It is hopeful that these datasets will contribute to the advancement of the field of plastic waste detection and encourage further research in this area.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111524"},"PeriodicalIF":1.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart Agriculture Dataset in a Tomato Cultivation under Different Irrigation Regimes 不同灌溉制度下番茄种植中的智能农业数据集

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Data in Brief Pub Date : 2025-03-27 DOI: 10.1016/j.dib.2025.111521

Laura Belli , Luca Davoli , Giulia Oddi , Luca Preite , Martina Galaverni , Tommaso Ganino , Gianluigi Ferrari

{"title":"Smart Agriculture Dataset in a Tomato Cultivation under Different Irrigation Regimes","authors":"Laura Belli , Luca Davoli , Giulia Oddi , Luca Preite , Martina Galaverni , Tommaso Ganino , Gianluigi Ferrari","doi":"10.1016/j.dib.2025.111521","DOIUrl":"10.1016/j.dib.2025.111521","url":null,"abstract":"<div><div>This dataset contains data collected in a tomato cultivation (namely, a Solanum lycopersicum L. cv. HEINZ 1301 cultivation) located at the “Azienda Sperimentale Stuard,” Parma, Italy, through an IoT infrastructure featuring Long Range Wide Area Network (LoRaWAN)-enabled commercial devices deployed in the crop during the summer 2023 period (June 29–September 13). The IoT architecture also controls the irrigation system deployed to manage the watering conditions in the tomato crop, in detail considering three different experimental lines (each one associated with a different irrigation regime): (i) line #1 was irrigated with a water quantity equal to the irrigation recommendation provided by a national cloud service, denoted as Irriframe and developed by the Water Boards Italian Association (ANBI); (ii) line #2 was irrigated with a water quantity equal to 60% of line #1; (iii) line #3 was irrigated with a water quantity equal to 30% of line #1. The dataset comprises 4 different CSV files. The first three files (named as “stuard_environmental_data.csv,” “stuard_water_meter_data.csv,” and “stuard_soil_data.csv”) contain the information sampled every 10 minute by the IoT devices deployed in the crop—one environmental sensor, three water meters, and three soil sensors. The fourth CSV file (named as “indicators.csv”) contains the values of agronomic indicators of interest, calculated daily and mainly depending on daily air temperature values: (i) the Growing Degree Days (GDD) index and (ii) the Heat Units indicators, both calculated on the collected experimental tomato crop data.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111521"},"PeriodicalIF":1.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A benchmark dataset of narrative student essays with multi-competency grades for automatic essay scoring in Brazilian Portuguese 一个基准数据集的叙事学生散文与多能力等级自动作文评分在巴西葡萄牙语

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Data in Brief Pub Date : 2025-03-27 DOI: 10.1016/j.dib.2025.111526

Hilário Oliveira , Rafael Ferreira Mello , Péricles Miranda , Hyan Batista , Moésio Wenceslau da Silva Filho , Thiago Cordeiro , Ig Ibert Bittencourt , Seiji Isotani

{"title":"A benchmark dataset of narrative student essays with multi-competency grades for automatic essay scoring in Brazilian Portuguese","authors":"Hilário Oliveira , Rafael Ferreira Mello , Péricles Miranda , Hyan Batista , Moésio Wenceslau da Silva Filho , Thiago Cordeiro , Ig Ibert Bittencourt , Seiji Isotani","doi":"10.1016/j.dib.2025.111526","DOIUrl":"10.1016/j.dib.2025.111526","url":null,"abstract":"<div><div>This paper describes the development of a new database comprising 1235 narrative essays written in Portuguese by 5th-grade students in Brazil. The corpus construction process involved three main steps: acquiring and transcribing photos of the essays, annotating them based on a real pre-defined correction rubric by experts considering four key writing competencies (formal language use, textual typology, thematic coherence, and textual cohesion), and resolving disagreements between the annotators. Two human experts manually evaluated each essay using a five-point scale (Level I: Complete lack of domain - Level V: Excellent mastery) aligned with the correction rubric. In cases of disagreement between the initial evaluators, a third expert facilitated the divergences resolution. To the best of our knowledge, this is the first publicly available dataset of elementary school essays in Brazilian Portuguese that features narrative writing samples with corresponding grades across multiple competencies commonly used in writing assessment. We believe this resource can contribute to developing automatic essay scoring systems tailored for evaluating narrative texts written in Brazilian Portuguese.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111526"},"PeriodicalIF":1.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tomato leaf dataset: A dataset for multiclass disease detection and classification 番茄叶片数据集：用于多类病害检测和分类的数据集

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Data in Brief Pub Date : 2025-03-27 DOI: 10.1016/j.dib.2025.111520

Ahmed Imtiaz , Fahad Bin Islam Swapnil , Syed Rayhan Masud , Debajyoti Karmaker

{"title":"Tomato leaf dataset: A dataset for multiclass disease detection and classification","authors":"Ahmed Imtiaz , Fahad Bin Islam Swapnil , Syed Rayhan Masud , Debajyoti Karmaker","doi":"10.1016/j.dib.2025.111520","DOIUrl":"10.1016/j.dib.2025.111520","url":null,"abstract":"<div><div>Agriculture is a cornerstone of Bangladesh's economy, with tomatoes being one of the most widely cultivated vegetables, producing approximately 368,000 tons annually. However, tomato plants are vulnerable to various diseases and pest infestations that can significantly reduce crop yield, posing a threat to farmers’ livelihoods. Early detection of these diseases, often visible through symptoms on the leaves, is critical for effective management. In this work, we present a dataset of 731 high-resolution images of tomato leaves affected by six common diseases, along with healthy samples, aimed at facilitating automated disease diagnosis using computer vision. The dataset is categorized into disease types such as Early Blight, Black Spot, Late Blight, Leaf Mold, Bacterial Spot, and Target Spot. This structured dataset offers a valuable resource for researchers developing machine learning models for disease classification and early detection. By making the dataset publicly available, we aim to accelerate research in precision agriculture and empower the development of AI-driven tools that can enhance tomato disease management, ultimately improving crop yields and supporting sustainable farming practices.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111520"},"PeriodicalIF":1.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-resolution bathymetric survey dataset of Yesik Lake, Kazakhstan: New insights into the morphology of the mountain water body 哈萨克斯坦叶西克湖高分辨率水深测量数据集：对山区水体形态的新认识

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Data in Brief Pub Date : 2025-03-26 DOI: 10.1016/j.dib.2025.111504

Kanat Samarkhanov , Nurlybek Zinabdin , Damir Amantayev , Arman Kabdeshev , Murat Muzdybaev , Aigerim Amantai , Kairat Abdrakhmanov , Aigerim Nurmagambetova , Alua Zhukenova , Aliya Kaisanova

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Baby names in Japan, 2019–2024: Common writings and their readings 日本的婴儿名字，2019-2024：常见的写作和解读

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Data in Brief Pub Date : 2025-03-26 DOI: 10.1016/j.dib.2025.111497

Yuji Ogihara

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An Indian UAV and leaf image dataset for integrated crop health assessment of soybean crop 用于综合评估大豆作物健康状况的印度无人机和叶片图像数据集

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Data in Brief Pub Date : 2025-03-26 DOI: 10.1016/j.dib.2025.111517

Sayali Shinde, Vahida Attar

{"title":"An Indian UAV and leaf image dataset for integrated crop health assessment of soybean crop","authors":"Sayali Shinde, Vahida Attar","doi":"10.1016/j.dib.2025.111517","DOIUrl":"10.1016/j.dib.2025.111517","url":null,"abstract":"<div><div>Soybean is an important oilseed crop, rich in protein and oil, often referred to as a ``cash crop'' or ``gold bean'' by Indian farmers. In Maharashtra, soybean cultivation spans over approximately 3.8 million hectares, producing 3.07 million tons, placing the state second in India for overall soybean production. However, despite of its significance, several issues such as weeds, diseases, and pests hamper the overall productivity of soybean. Addressing these challenges faced by soybean growers it is essential to enhance yield and improve the crop's overall potential Currently, the farming sector is transitioning towards Agriculture 5.0, also known as digital farming. This approach utilizes data-driven technologies, such as artificial intelligence and computer vision, to transform the agriculture sector. These technologies enable the automation of several farming tasks. To develop accurate and robust machine learning/deep learning models high quality datasets are needed.</div><div>With this aim, we have created a comprehensive dataset of soybean crop images affected by diseases and pest attacks from original fields of Maharashtra region located in India. Data acquisition was conducted across two seasons through aerial as well as ground-based approaches. The dataset is enriched with 4 types of diseases and 1 pest attack. The proposed dataset will serve as a valuable resource for training and testing machine learning and deep learning models ,enabling accurate detection and classification of diseases and pests attack damage.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111517"},"PeriodicalIF":1.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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A catchment-scale dataset of soil properties and their mid-infrared spectra 流域尺度土壤特性及其中红外光谱数据集

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Data in Brief Pub Date : 2025-03-26 DOI: 10.1016/j.dib.2025.111510

Alexandre M.J.-C. Wadoux , Felix Stumpf , Thomas Scholten

{"title":"A catchment-scale dataset of soil properties and their mid-infrared spectra","authors":"Alexandre M.J.-C. Wadoux , Felix Stumpf , Thomas Scholten","doi":"10.1016/j.dib.2025.111510","DOIUrl":"10.1016/j.dib.2025.111510","url":null,"abstract":"<div><div>The dataset presents information on soil properties and their associated mid-infrared spectra for a drainage basin of 4.2 km<sup>2</sup>, referred to as Upper Badong catchment (31°1′24′′N, 110°20′35′′E) in the Hubei province, China. Data were collected for topsoil in a highly diverse terrace catchment composed of woodland, cropland and small farm building. Soil properties included in this dataset are pH, texture (i.e. clay, silt and sand content), total carbon, organic carbon and CaCO<sub>3</sub>. In addition, the soil samples were scanned in the mid-infrared range. The data collection processed involved three field campaigns during 2013 and 2014 where topsoil samples were collected in a standardized way across all sites, and soil analyses in the laboratory of soil science following standard procedures. The dataset offers insights into the spatial variation of soil properties in a highly diverse catchment of central China. Researchers interested in soil research can use this dataset for various purposes, including building digital soil mapping models or soil spectroscopic models, benchmarking of models with other datasets, and research in soil erosion modelling.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111510"},"PeriodicalIF":1.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0