Data in Brief最新文献_第8页

King Abdulaziz University Hospital Capsule dataset: A novel small-bowel endoscopic image repository from Saudi Arabia. 阿卜杜勒阿齐兹国王大学医院胶囊数据集：来自沙特阿拉伯的新型小肠内窥镜图像库。

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Data in Brief Pub Date : 2024-11-08 eCollection Date: 2024-12-01 DOI: 10.1016/j.dib.2024.111093

Hamza Ghandorh, Hamza H Bali, Wael M S Yafooz, Wadii Boulila, Majid Alsahafi

{"title":"King Abdulaziz University Hospital Capsule dataset: A novel small-bowel endoscopic image repository from Saudi Arabia.","authors":"Hamza Ghandorh, Hamza H Bali, Wael M S Yafooz, Wadii Boulila, Majid Alsahafi","doi":"10.1016/j.dib.2024.111093","DOIUrl":"10.1016/j.dib.2024.111093","url":null,"abstract":"<p><p>Wireless Capsule Endoscopy (WCE) has fundamentally transformed diagnostic methodologies for small-bowel (SB) abnormalities, providing a comprehensive and non-invasive gastrointestinal assessment in contrast to conventional endoscopic procedures. The King Abdulaziz University Hospital Capsule (KAUHC) dataset comprises annotated WCE images specifically curated for Saudi Arabian residents. Comprising 10.7 million frames derived from 157 studies, KAUHC has been classified into Normal, Arteriovenous Malformations, and Ulcer categories. Following the application of specific inclusion and exclusion criteria, 3301 labeled frames derived from WCE 86 studies were identified. Upon admission of patients, the data collection phase of KAUHC was initiated, involving the administration of the OMOM capsule and the use of the OMOM recording device for video documentation. A thorough evaluation of these recordings was undertaken by multiple gastroenterologists to identify any pathological abnormalities. The identified observations are subsequently extracted, categorized, and prepared for validation using Machine Learning (ML) classifiers. The dataset aims not only to address the scarcity of annotated endoscopic imaging resources in the Middle East but also to advance the development of diagnostic tools for ML applications in SB abnormalities and exploratory research on gastrointestinal diseases.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111093"},"PeriodicalIF":1.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779584","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

Analytical data on three Martian simulants. 三个火星模拟物的分析数据。

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Data in Brief Pub Date : 2024-11-08 eCollection Date: 2024-12-01 DOI: 10.1016/j.dib.2024.111099

Nicole Costa, Alessandro Bonetto, Patrizia Ferretti, Bruno Casarotto, Matteo Massironi, Francesca Altieri, Jacopo Nava, Marco Favero

{"title":"Analytical data on three Martian simulants.","authors":"Nicole Costa, Alessandro Bonetto, Patrizia Ferretti, Bruno Casarotto, Matteo Massironi, Francesca Altieri, Jacopo Nava, Marco Favero","doi":"10.1016/j.dib.2024.111099","DOIUrl":"10.1016/j.dib.2024.111099","url":null,"abstract":"<p><p>The preparation of planetary missions as well as the analysis of their data require a wide use of planetary simulants. They are very important for both testing mission operations and payloads, and for interpreting remote sensing data. In this work, a detailed analysis of three commercially available simulants of Martian dust and regolith is presented. Indeed, up to date, a complete data set related to their chemical, mineralogical, granulometric and spectral characters is not fully provided by their distribution and sales companies. Our dataset regards the Mars Global (MGS-1) High-Fidelity Martian Dirt Simulant [1], the Mojave Mars Simulant MMS-1 [2] and the Enhanced Mars Simulant (MMS-2) [2]. Being essential for ensuring consistency and enabling data comparison, all the chosen Martian simulants underwent the same analytical process. Grainsize data were collected using a Laser Diffraction Particle Size Analyzer. Chemical analysis was performed by Inductively Coupled Plasma Mass Spectroscopy (ICP-MS). Mineralogical analysis was carried out by X-Ray powder Diffractometry (XRD). Moreover, the largest particles of MGS-1 simulant were analyzed with the Scanning Electron Microscope (SEM-EDS) in order to confirm their chemical composition. Finally, the spectral acquisitions in the VNIR-SWIR range were taken by two Headwall Photonics hyperspectral imaging cameras. This complete series of data integrating pre-existing ones (e.g., Cannon et al. [1] and Karl et al. [2]) can in the future be used to allow a straightful choice of the right simulant for biological and life-support experiments and potential testing of mission instruments, to help inferring the composition of the Martian surface from remote sensing data, and to create new simulants or adjust the existing ones in order to get closer to the known Martian regolith variability and eventually new compositional information provided by future missions.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111099"},"PeriodicalIF":1.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779673","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

Dataset of inertial measurements for writing Punjabi characters using IMU sensors. 使用IMU传感器编写旁遮普字符的惯性测量数据集。

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Data in Brief Pub Date : 2024-11-08 eCollection Date: 2024-12-01 DOI: 10.1016/j.dib.2024.111083

AnchalPreet Sharma, Harsh Kumar, Lakhjeet Kaur, Ramakant Kumar, Pravin Kumar

{"title":"Dataset of inertial measurements for writing Punjabi characters using IMU sensors.","authors":"AnchalPreet Sharma, Harsh Kumar, Lakhjeet Kaur, Ramakant Kumar, Pravin Kumar","doi":"10.1016/j.dib.2024.111083","DOIUrl":"10.1016/j.dib.2024.111083","url":null,"abstract":"<p><p>This study introduces a comprehensive methodology for gathering datasets to recognize handwritten Punjabi alphabets, utilizing Inertial Measurement Units (IMUs) to capture the dynamic movement patterns inherent in handwriting. The approach considers the diverse writing styles found across Punjabi writers, which presents unique challenges due to regional variations in script. The dataset and collection system are designed to enhance recognition accuracy by harnessing this diversity. The data collection process involved recording handwriting movements from multiple participants, ensuring the dataset reflects a wide range of writing styles. By leveraging IMUs, the system tracks detailed handwriting motions, enhancing character recognition accuracy. The use of IMUs allows for the detailed tracking of handwriting movements, which is crucial for improving the accuracy of character recognition. Preliminary experimental results indicate that the dataset not only effectively captures the nuances of handwritten Punjabi but also demonstrates potential in recognizing handwritten English alphabets within the Indian context. This research contributes significantly to the field of pattern recognition, offering insights that could lead to the development of more robust handwriting recognition systems particularly suited for various linguistic and cultural settings.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111083"},"PeriodicalIF":1.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11617984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142784449","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

An ergometer dataset to measure muscle bioenergetics with magnetic resonance techniques. 一个用磁共振技术测量肌肉生物能量的测力计数据集。

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Data in Brief Pub Date : 2024-11-07 eCollection Date: 2024-12-01 DOI: 10.1016/j.dib.2024.111114

Usman Rehman, Gwenaelle Begue, Armin Ahmadi, Paolo Taboga, Jorge Gamboa, Baback Roshanravan, Thomas Jue

引用次数: 0

Historical wind speed dataset of meteorological mast station in Khartoum. 喀土穆气象桅杆站历史风速数据集。

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Data in Brief Pub Date : 2024-11-07 eCollection Date: 2024-12-01 DOI: 10.1016/j.dib.2024.111115

Abubaker Younis, Hazim Elshiekh, Yassir Yassin, Ali Omer, Elfadil Biraima

{"title":"Historical wind speed dataset of meteorological mast station in Khartoum.","authors":"Abubaker Younis, Hazim Elshiekh, Yassir Yassin, Ali Omer, Elfadil Biraima","doi":"10.1016/j.dib.2024.111115","DOIUrl":"10.1016/j.dib.2024.111115","url":null,"abstract":"<p><p>The data demonstration article presented here showcases three months of wind speed field records for Khartoum city from June to August 2017. These records were obtained from the SOBA-D161094 meteorological mast station, located within the premises of the National Energy Research Center of Sudan. Using the two-parameter Weibull distribution, the scale and shape parameters estimated by the method of moments for this dataset were 4.175 m/s and 2.099, respectively, with a coefficient of determination of 0.975, as provided in the associated literature. The accuracy of the data was verified using spatial wind speed information from the MERRA-2 database, compiled by a NASA observation satellite, with a root mean square error between the ground and remote sensing datasets found to be 0.385. Additionally, the Kolmogorov-Smirnov test suggests that the two samples are drawn from the same population and statistical distribution. Based on the Weibull density function, the mean power transported by wind and the maximum mean power that can be extracted by the turbine were 103.45 W and 61.3 W, respectively. The primary objective of this work is to provide the data in a format that enables its use as a benchmark or for reuse in various research endeavors. Special emphasis is placed on facilitating studies related to the parameter estimation of wind speed statistical distribution models. This approach is akin to the utilization of the RTC France solar cell dataset, which is commonly employed for parameter extraction in equivalent circuit models. The added value of this data lies in its potential to provide information that could reveal unrecognized opportunities for the domestic generation of wind power.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111115"},"PeriodicalIF":1.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779582","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

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

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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

{"title":"RiGaD: An aerial dataset of rice seedlings for assessing germination rates and density","authors":"Trong Hieu Luu , Hoang-Long Cao , Quang Hieu Ngo , Thanh Tam Nguyen , Ilias El Makrini , Bram Vanderborght","doi":"10.1016/j.dib.2024.111118","DOIUrl":"10.1016/j.dib.2024.111118","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111118"},"PeriodicalIF":1.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707124","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

A new dataset for spatial, temporal, and tactical analysis of female handball player movements. 一个新的数据集，用于空间，时间和战术分析的女子手球运动员的动作。

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Data in Brief Pub Date : 2024-11-06 eCollection Date: 2024-12-01 DOI: 10.1016/j.dib.2024.111116

Raul Montoliu, Pere Urbón-Bayes, Gabriel Daza, Abraham Batalla-Gavalda

引用次数: 0

Data on behavior and environmental impact of compostable packaging materials in full-scale industrial composting conditions. 可堆肥包装材料在全面工业堆肥条件下的行为和环境影响数据。

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Data in Brief Pub Date : 2024-11-06 eCollection Date: 2024-12-01 DOI: 10.1016/j.dib.2024.111102

Emmanuelle Gastaldi, Felipe Buendia, Paul Greuet, Sandra Domenek

引用次数: 0

Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley. 为罗马尼亚Mostiștea盆地和多瑙河流域的考古研究建立空间数据储存库。

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Data in Brief Pub Date : 2024-11-06 eCollection Date: 2024-12-01 DOI: 10.1016/j.dib.2024.111119

Cornelis Stal, Cristina Covătaru, Quinten De Wolf, Theodor Ignat, Dmytro Pecheniuk, Cătălin Lazăr

{"title":"Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley.","authors":"Cornelis Stal, Cristina Covătaru, Quinten De Wolf, Theodor Ignat, Dmytro Pecheniuk, Cătălin Lazăr","doi":"10.1016/j.dib.2024.111119","DOIUrl":"https://doi.org/10.1016/j.dib.2024.111119","url":null,"abstract":"<p><p>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This paper presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111119"},"PeriodicalIF":1.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11647147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142834381","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

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

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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

{"title":"An extensive image dataset for deep learning-based classification of rice kernel varieties in Bangladesh","authors":"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","doi":"10.1016/j.dib.2024.111109","DOIUrl":"10.1016/j.dib.2024.111109","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111109"},"PeriodicalIF":1.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707125","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