Data in Brief最新文献_第2页

4D (space + time) datasets of spruce wood enzymatic hydrolysis 云杉木材酶解的4D（空间+时间）数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-20 DOI: 10.1016/j.dib.2026.112489

Solmaz Hossein Khani, Maxime Corré, Khadidja Ould Amer, Noah Remy, Berangère Lebas, Anouck Habrant, Gabriel Paës, Yassin Refahi

The conversion of lignocellulosic biomass from plant cell walls into bioproducts can contribute to reducing dependence on fossil sources and achieving sustainable development. Biotechnological conversion of lignocellulosic biomass has several advantages over other conversion approaches such as thermochemical and chemical conversions. These advantages include improved efficiency and specificity for desired products, ecological compatibility and reduced toxicity. Enzymatic transformation is a key step in biotechnological conversion. To achieve a cost-effective conversion, a comprehensive understanding of cell wall enzymatic hydrolysis is required. Despite progress, the enzymatic hydrolysis at microscale is comparatively understudied and lacks comprehensive investigation. Addressing this gap requires collection of time-lapse image datasets of cell wall enzymatic hydrolysis which is a technically demanding task. Furthermore, accurate processing of the time-lapse images to identify and track individual cell walls is particularly challenging, notably because of the sample drift present in the images. Recently, an efficient image processing pipeline, called AIMTrack, has been developed which uses an enhanced divide-and-conquer strategy to divide time-lapse images into clusters whose sizes are dynamically adjusted to the deconstruction extent. The image registrations are then limited to clusters and the resulting transformations are combined to correct sample drift across time-lapse images. Subsequently AIMTrack provides segmentation of time-lapse images where voxels belonging to the same cell walls are labelled with a unique identifier. The time-lapse image datasets presented here consist of time-lapse images of spruce wood cell walls acquired during enzymatic hydrolysis using a cellulolytic enzyme cocktail at two enzyme loadings of 15 and 30 FPU/g biomass. Control time-lapse datasets which are acquired under the identical conditions, but without addition of enzymes, are also included. Both control and hydrolysis datasets are processed using AIMTrack to track the cell walls from time-lapse images. The generated segmentations are also provided.

将植物细胞壁中的木质纤维素生物质转化为生物产品有助于减少对化石资源的依赖并实现可持续发展。与热化学和化学转化等其他转化方法相比，木质纤维素生物质的生物技术转化具有若干优势。这些优点包括提高效率和对所需产品的特异性，生态相容性和降低毒性。酶转化是生物技术转化的关键步骤。为了实现经济有效的转化，需要对细胞壁酶解有全面的了解。尽管取得了进展，但在微观尺度上的酶解研究相对不足，缺乏全面的研究。解决这一差距需要收集细胞壁酶解的延时图像数据集，这是一项技术要求很高的任务。此外，准确处理延时图像以识别和跟踪单个细胞壁尤其具有挑战性，特别是因为图像中存在样品漂移。近年来，人们开发了一种高效的图像处理管道AIMTrack，该管道采用一种增强的分而治之策略，将延时图像分成簇，并根据解构程度动态调整簇的大小。然后将图像配准限制在簇中，并将产生的转换组合在一起以纠正跨延时图像的样本漂移。随后，AIMTrack提供了延时图像的分割，其中属于相同细胞壁的体素被标记为唯一标识符。本文提供的延时图像数据集包括使用纤维素水解酶混合物在15和30 FPU/g生物量的两种酶负荷下酶水解过程中获得的云杉木细胞壁的延时图像。还包括在相同条件下获得的控制性时移数据集，但没有添加酶。使用AIMTrack对控制和水解数据集进行处理，从延时图像中跟踪细胞壁。还提供了生成的分段。

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引用次数: 0

ConcreteCARB: A comprehensive image dataset of concrete carbonation for computer vision tasks ConcreteCARB：用于计算机视觉任务的混凝土碳化的综合图像数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-20 DOI: 10.1016/j.dib.2026.112493

José A. Guzmán-Torres, Sandra del C. Arguello-Hernández, Francisco J. Domínguez-Mota, Gerardo Tinoco-Guerrero, Elia M. Alonso-Guzmán

The ConcreteCARB dataset provides a comprehensive repository of 903 high-resolution images of concrete surfaces evaluated using the phenolphthalein test for carbonation detection. This data was collected under controlled laboratory conditions and aims to support artificial intelligence applications in civil engineering, especially in structural health monitoring tasks. The images are systematically organized into two distinct classes: “Carbonated Samples” and “No Carbonation Presence,” enabling binary classification approaches. All samples were manually tested, split, and visually labelled by expert engineers to ensure reliable ground-truth classification, in accordance with standardized procedures. The dataset includes images of concrete prism elements fabricated with varying mix designs, incorporating different water-cement ratios and additives, such as industrial silica waste and natural admixtures derived from Opuntia ficus-indica. The specimens were subjected to natural atmospheric carbonation conditions for 180 days, and their carbonation fronts were revealed by phenolphthalein staining. The samples were then split manually with a chisel and hammer, and photographic documentation was performed with a Samsung SM-S901U1 smartphone using predefined settings to ensure consistency and quality across the dataset. ConcreteCARB is intended for researchers, engineers, and data scientists working on machine learning, deep learning, and computer vision solutions for concrete diagnostics. It provides valuable training and benchmarking data for the development of automated detection, classification, and segmentation models for carbonation damage assessment. Furthermore, the dataset can serve as a foundational tool for cross-comparative studies on the efficacy of AI techniques in materials degradation analysis. The openly accessible nature of the dataset through a public repository supports reproducibility and encourages the extension of AI applications in concrete durability and sustainability studies.

ConcreteCARB数据集提供了903张混凝土表面高分辨率图像的综合存储库，使用酚酞测试进行碳化检测。这些数据是在受控的实验室条件下收集的，旨在支持人工智能在土木工程中的应用，特别是在结构健康监测任务中。这些图像被系统地组织成两个不同的类别：“碳酸化样本”和“无碳酸化存在”，这使得二元分类方法成为可能。所有样品都由专家工程师手工测试、分离和视觉标记，以确保根据标准化程序进行可靠的真实分类。该数据集包括混凝土棱柱体的图像，这些棱柱体采用不同的混合设计，采用不同的水灰比和添加剂，如工业二氧化硅废料和源自无花果树的天然外加剂。将样品置于自然大气碳酸化条件下180 d，通过酚酞染色显示其碳酸化锋。然后用凿子和锤子手动分割样本，并使用三星SM-S901U1智能手机进行摄影记录，使用预定义设置确保数据集的一致性和质量。ConcreteCARB适用于研究机器学习、深度学习和混凝土诊断计算机视觉解决方案的研究人员、工程师和数据科学家。它为开发用于碳酸化损害评估的自动检测、分类和分割模型提供了有价值的训练和基准数据。此外，该数据集可以作为人工智能技术在材料降解分析中的功效交叉比较研究的基础工具。通过公共存储库开放访问数据集的特性支持再现性，并鼓励人工智能应用在混凝土耐久性和可持续性研究中的扩展。

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引用次数: 0

Dataset of in-situ meteorological measurements for urban wind energy assessment in the southern region of the Dominican Republic 多米尼加共和国南部地区城市风能评估的现场气象测量数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-19 DOI: 10.1016/j.dib.2026.112482

Alexander Vallejo-Díaz , Idalberto Herrera-Moya , Edwin Garabitos-Lara , Héctor David Morbán-Ramírez , Adermim Jhoswar Severino-Simeón , Anders Malmquist

This dataset provides in-situ wind measurements collected between March 2024 and June 2025 from four urban locations in the southern region of the Dominican Republic: San Cristóbal, Azua, Barahona, and San Juan de la Maguana. Measurements were performed using Davis Instruments Vantage PRO2 meteorological stations, strategically installed to characterize the urban wind resource for potential microgeneration applications. The dataset includes wind speed, wind direction, air temperature, relative humidity, and atmospheric pressure. Data processing involved quality control procedures, gap filling through interpolation techniques, and subsequent analysis for wind characterization. The analysis was carried out using WRPLOT View – Wind Rose Plotting Software Version 9.2.0 for wind rose generation, HOMER Pro – Microgrid Analysis Tool, Version 3.18.4 for renewable resource assessment, and Microsoft Excel for parameterization of the Weibull distribution function. In addition, derived metrics such as theoretical wind potential and the theoretically available wind potential were calculated for each location.

This dataset can serve as a valuable resource for preliminary renewable energy feasibility studies, particularly for screening and comparative assessments of small-scale or distributed generation in urban environments. It also supports urban energy planning and can be used to inform computational fluid dynamics (CFD) studies of urban wind flows, for example through boundary condition definition, model calibration, or comparative analysis, rather than direct validation of highly turbulent urban wind fields, and the design of hybrid wind–solar systems. Beyond energy applications, the data may be applied to urban climate studies, including the assessment of diurnal and seasonal variability and heat island effects, and to modeling the dispersion of air pollution in complex urban settings.

该数据集提供了在2024年3月至2025年6月期间从多米尼加共和国南部地区的四个城市地点收集的现场风测量数据：圣Cristóbal，阿祖阿，巴拉霍纳和圣胡安德拉马瓜纳。测量使用的是Davis Instruments Vantage PRO2气象站，这些气象站战略性地安装在城市风力资源特征上，用于潜在的微发电应用。数据集包括风速、风向、气温、相对湿度和大气压力。数据处理包括质量控制程序，通过插值技术填充间隙，以及随后的风特性分析。分析采用WRPLOT View -风玫瑰生成软件9.2.0版本，HOMER Pro -微电网分析工具3.18.4版本进行可再生资源评价，Microsoft Excel进行威布尔分布函数参数化。此外，还计算了每个地点的理论风势和理论可用风势等衍生指标。该数据集可作为初步可再生能源可行性研究的宝贵资源，特别是用于筛选和比较城市环境中小规模或分布式发电的评估。它还支持城市能源规划，并可用于城市风流的计算流体动力学（CFD）研究，例如通过边界条件定义、模型校准或比较分析，而不是直接验证高度湍流的城市风场，以及设计混合风能-太阳能系统。除了能源应用之外，这些数据还可用于城市气候研究，包括评估日和季节变率以及热岛效应，并可用于模拟复杂城市环境中空气污染的扩散。

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引用次数: 0

Towards sustainable management of Xylella fastidiosa vectors: An annotated image dataset for automated in-field detection of Aphrophoridae foam 对苛刻木杆菌载体的可持续管理：一个用于自动现场检测泡沫蚜的注释图像数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-19 DOI: 10.1016/j.dib.2026.112477

Michele Elia , Angelo Cardellicchio , Michele Paradiso , Giuseppe Veronico , Arianna Rana , Antonio Petitti , Vito Renò , Simone Pascuzzi , Annalisa Milella

Insects feeding on xylem sap, such as adult Aphrophoridae spittlebugs, are vectors of the plant pathogenic xylem-limited bacterium Xylella fastidiosa (Xf), a causal agent of a number of severe diseases, including the Olive Quick Decline Syndrome (OQDS), which has decimated olive trees in the Mediterranean region. The Aphrophoridae life cycle and behaviour feature a weak stage, known as the juvenile stage, in which the insects live solitary on stems covered in a self-produced foamy fluid (froth) that protects them from dehydration and temperature stress. Juvenile vectors are ideal targets for a control intervention aimed at reducing transmission by adults. This paper presents the first, to the best of our knowledge, image dataset framing spittlebug froth samples in the field for the purpose of automated Aphrophoridae nymph identification. Images were captured using different devices including a consumer-grade RGB-D sensor, a digital reflex camera, and a smartphone camera. The dataset comprises 365 colour images, focusing on spittlebug foam. 211 of these images were captured in April 2024 during a two-day campaign. For these 211 images, a manual semantic annotation was performed, generating PNG binary masks that precisely distinguish spittlebug foam pixels from the background. To further enhance usability, labels are also provided in YOLO (You Only Look Once) format as text files, both for segmentation and object detection. The remaining 154 images were collected during a separate two-day campaign in 2025. These images are unannotated and are intended for further testing purposes. Overall, the dataset enables the development of both semantic segmentation models and object detectors for automated froth detection in natural images, thus facilitating the early identification of potentially harmful insects in sustainable pest management and control systems.

以木质部汁液为食的昆虫，如成年aphaphoridae口吐虫，是植物致病性木质部限制细菌苛养木杆菌（Xf）的媒介，Xf是许多严重疾病的病原体，包括橄榄树快速衰退综合征（OQDS），它使地中海地区的橄榄树大量死亡。aphaphoridae的生命周期和行为特征是一个较弱的阶段，称为幼年期，在这个阶段，昆虫独自生活在覆盖着自我产生的泡沫液体（泡沫）的茎上，以保护它们免受脱水和温度压力。青少年病媒是旨在减少成人传播的控制干预的理想目标。本文提出了第一个，据我们所知，图像数据集框架的吐沫虫泡沫样本在现场，目的是自动识别Aphrophoridae仙女。使用不同的设备拍摄图像，包括消费级RGB-D传感器，数码反光相机和智能手机相机。该数据集包括365张彩色图像，重点是吐痰虫泡沫。其中211张是在2024年4月为期两天的活动中拍摄的。对于这211张图像，我们执行了手动语义注释，生成了PNG二进制掩码，可以精确地将吐沫虫泡沫像素与背景区分开来。为了进一步增强可用性，标签还以YOLO（你只看一次）格式作为文本文件提供，用于分割和对象检测。其余154幅图像是在2025年的一个单独的为期两天的活动中收集的。这些图像未加注释，用于进一步测试。总体而言，该数据集支持语义分割模型和对象检测器的开发，用于自然图像中的自动泡沫检测，从而促进可持续害虫管理和控制系统中潜在有害昆虫的早期识别。

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引用次数: 0

Guarding against malicious biased threats (GAMBiT) datasets: Revealing cognitive bias in human-subjects red-team cyber range operations 防范恶意偏见威胁（GAMBiT）数据集：揭示人类受试者红队网络范围行动中的认知偏见

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-18 DOI: 10.1016/j.dib.2026.112476

Brandon Beltz , Jim Doty , Yvonne Fonken , Nikolos Gurney , Brett Israelsen , Nathan Lau , Stacy Marsella , Rachelle Thomas , Stoney Trent , Peggy Wu , Ya-Ting Yang , Quanyan Zhu

We present datasets from three large-scale human-subject experiments involving red-team hacking in a cyber range in the Guarding Against Malicious Biased Threats (GAMBiT) project. Across Experiments 1-3 (July 2024-March 2025), 19-20 skilled attackers per experiment conducted two 8-hour days of self-paced operations in a simulated enterprise network (SimSpace Cyber Force Platform) while collecting multi-modal data: self-reports (background, demographics, psychometrics), operational notes, terminal histories, key logs, network packet captures (PCAP), and NIDS alerts (Suricata). Each participant began from a standardized Kali Linux VM and pursued realistic objectives (e.g., target discovery and data exfiltration) under controlled constraints. Derivative curated logs and labels are included. The combined data release supports research on attacker behavior modeling, bias-aware analytics, and method benchmarking. Data are available via IEEE DataPort entries for Experiments 1-3.

我们展示了在防范恶意偏见威胁（GAMBiT）项目中涉及红队黑客在网络范围内的三个大规模人类受试者实验的数据集。在实验1-3期间（2024年7月至2025年3月），每个实验有19-20名熟练的攻击者在模拟企业网络（SimSpace Cyber Force Platform）中进行了两个8小时的自定义操作，同时收集了多模态数据：自我报告（背景，人口统计，心理测量），操作笔记，终端历史，关键日志，网络数据包捕获（PCAP）和NIDS警报（Suricata）。每个参与者都从一个标准化的Kali Linux VM开始，在受控的约束下追求现实的目标（例如，目标发现和数据泄露）。衍生策划日志和标签包括在内。合并的数据发布支持攻击者行为建模、偏见感知分析和方法基准测试的研究。实验1-3的数据可通过IEEE数据端口条目获得。

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引用次数: 0

Survey data on students’ perceptions, knowledge, and use of learning analytics (LA) and generative artificial intelligence (GenAI) for the personalization of learning 调查学生对学习分析（LA）和生成式人工智能（GenAI）个性化学习的认知、知识和使用情况

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-16 DOI: 10.1016/j.dib.2026.112474

Diego Calderón-Garrido, Carles Lindín, Lluís Parcerisa

This data article describes the dataset from the project ``Teacher Mediation in the Integration of Learning Analytics and Artificial Intelligence in Compulsory Secondary Education: Evidence, Strategies, and Practices for the Personalization of Education'' (MedIAP). The aim of this research project is to analyse students’ perceptions, knowledge, and use of Learning Analytics (LA) and Generative Artificial Intelligence (GenAI) for the personalization of learning in compulsory secondary education in Catalonia. Data were collected through an online survey, resulting in a final sample of 982 secondary education students. The description of the dataset in this article is structured in two main parts. The first provides a descriptive analysis of all survey items, presented using tables and figures. The second focuses on the construction of measurement scales, developed through Confirmatory Factor Analysis (CFA) and Multigroup Confirmatory Factor Analysis (MG-CFA). This dataset will be of interest to researchers across various disciplines, particularly those focused on Learning Analytics and Artificial Intelligence in educational contexts.

这篇数据文章描述了来自“义务中等教育中学习分析和人工智能整合中的教师中介：个性化教育的证据、策略和实践”（MedIAP）项目的数据集。该研究项目的目的是分析学生对学习分析（LA）和生成式人工智能（GenAI）的认知、知识和使用，以实现加泰罗尼亚义务中等教育学习的个性化。数据通过在线调查收集，最终样本为982名中学学生。本文中对数据集的描述主要分为两个部分。第一份报告对所有调查项目进行了描述性分析，使用表格和数字。第二部分侧重于通过验证性因子分析（CFA）和多组验证性因子分析（MG-CFA）构建测量量表。该数据集将对各个学科的研究人员感兴趣，特别是那些专注于学习分析和教育环境中的人工智能的研究人员。

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引用次数: 0

Measurement data from full-scale fire experiments of battery electric vehicles and internal combustion engine vehicles 纯电动汽车和内燃机汽车全尺寸着火实验测量数据

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-16 DOI: 10.1016/j.dib.2026.112471

Nathaniel G. Sauer, Matthew J. DiDomizio, Richard M. Kesler, Shruti Ghanekar, Parham Dehghani, Gavin P. Horn, Adam Barowy

Over time, the burning behaviour of passenger vehicles has been influenced by continuous advancements in vehicle design, including changes in materials, manufacturing processes, and the integration of modern lithium-ion battery powertrains. Fire protection engineers, first responders, and other safety professionals currently lack sufficient data to determine whether existing fire protection system designs and firefighting practices effectively mitigate the burning behaviour of modern vehicle fires or to adapt these approaches to any measurable changes in fire dynamics. Eighteen full-scale vehicle fire experiments were conducted in a laboratory environment to obtain a novel data set describing the fire size, thermal hazards, and evolved smoke and particulate species resulting from vehicle fires. Vehicle mass, gas temperature, heat flux, sorbent tube, Fourier-transform infrared spectrometer, and suppression waterflow data are provided in tabular format. Eight vehicle models across six manufacturers were selected based on the most popular battery electric and gasoline powered compact and mid-sized recently sold in North America at the time of writing. Experiments consisted of nine free-burn experiments wherein the gasoline vehicles were ignited in the engine compartment and the electric vehicle battery packs were induced into thermal runaway; these fires progressed unabated until the occurrence of natural flame extinction. Another four experiments simulated electric vehicle fire suppression using ordinary water and traditional vehicle fire suppression techniques. In one additional experiment, an encapsulator firefighting agent was added to the water. The final four experiments consisted of deploying fire blankets over the burning vehicles a single strategy or in combination with water simultaneously applied from beneath. There are many potential uses of this data, but primary uses are expected to include revision of design criteria and guidance within the fire protection engineering community, strategic and tactical decision aids for vehicle fire incident operations, validation of existing (and development of new) fire behavior models, and guidance to vehicle manufacturers for improved fire safety design.

随着时间的推移，乘用车的燃烧行为受到车辆设计不断进步的影响，包括材料、制造工艺的变化以及现代锂离子电池动力系统的集成。消防工程师、急救人员和其他安全专业人员目前缺乏足够的数据来确定现有的消防系统设计和消防实践是否有效地减轻了现代车辆火灾的燃烧行为，或者使这些方法适应火灾动力学的任何可测量变化。在实验室环境中进行了18次全尺寸车辆火灾实验，以获得描述火灾规模、热危害以及车辆火灾产生的烟雾和颗粒种类的新数据集。车辆质量、气体温度、热流、吸附管、傅里叶变换红外光谱仪、抑制水流数据以表格形式提供。在撰写本文时，根据最近在北美销售的最受欢迎的电池电动和汽油动力紧凑型和中型车，选择了6家制造商的8款车型。实验包括9个自由燃烧实验，分别在发动机舱内点燃汽油车，诱导电动汽车电池组热失控；这些大火不断发展，直到火焰自然熄灭。另外四个实验分别采用普通水和传统车辆灭火技术模拟电动汽车灭火。在另一项实验中，向水中加入了一种封装式灭火剂。最后四个实验包括在燃烧的车辆上铺上防火毯，一种策略，或者与从下面同时浇水相结合。这些数据有许多潜在的用途，但主要用途预计包括修订设计标准和指导消防工程社区，为车辆火灾事故操作提供战略和战术决策辅助，验证现有（和开发新的）火灾行为模型，并指导车辆制造商改进消防安全设计。

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引用次数: 0

Dataset of 12,161 steel rebar tests from sudanese construction projects (2016-2022) 2016-2022年苏丹建筑项目12161根钢筋试验数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-15 DOI: 10.1016/j.dib.2026.112469

Amged O. Abdelatif, Abdelrahim H. Abdelrahim, Gamar-Aldwla S. Shangray, Mohammed-Alfatih Mustafa, Mustafa M. Abaker, Yahia A. Idris, Abdelrahim M. Yousif

This data article describes a comprehensive dataset comprising 12,161 individual steel reinforcement bar tensile tests (3,898 test reports) collected from various construction projects across Sudan between 2016 and 2022. The data was systematically extracted from official test reports generated by the University of Khartoum, Faculty of Engineering, Department of Civil Engineering, Material and Structures Testing Laboratory. The purpose of this dataset is to establish a verified, large-scale baseline of material performance for Sudanese reinforcement steel, providing transparent and verifiable raw values of key mechanical and dimensional properties for locally sourced rebars with tested diameters ranging from 8 mm to 32 mm. This data is intended for reuse to conduct rigorous analyses on steel reinforcement quality and characteristic properties in Sudan, offering a unique baseline for regional construction quality and providing a representative performance benchmark applicable to other developing countries.

这篇数据文章描述了一个全面的数据集，其中包括2016年至2022年间从苏丹各地的各种建筑项目收集的12,161个单独的钢筋拉伸试验（3,898个试验报告）。数据系统地摘自喀土穆大学土木工程系工程学院、材料和结构测试实验室编制的正式测试报告。该数据集的目的是为苏丹钢筋建立一个经过验证的大规模材料性能基线，为当地采购的钢筋提供透明和可验证的关键机械和尺寸性能原始值，测试直径范围为8毫米至32毫米。这些数据旨在重新利用，以便对苏丹的钢筋质量和特性进行严格分析，为区域建筑质量提供独特的基线，并提供适用于其他发展中国家的具有代表性的性能基准。

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引用次数: 0

BraTioUS: A multicenter dataset of baseline intraoperative brain tumor ultrasound images br：一个多中心的基线术中脑肿瘤超声图像数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-15 DOI: 10.1016/j.dib.2026.112478

Olga Esteban-Sinovas , Rosario Sarabia , Ignacio Arrese , Vikas Singh , Prakash Shett , Aliasgar Moiyadi , Ilyess Zemmoura , Massimiliano Del Bene , Arianna Barbotti , Francesco DiMeco , Timothy Richard West , Brian Vala Nahed , Giuseppe Roberto Giammalva , Santiago Cepeda

The BraTioUS (Brain Tumor Intraoperative Ultrasound) dataset [1] is a large-scale, multicenter, and publicly available collection of intraoperative ultrasound (ioUS) images acquired during glioma surgeries. Created through an international collaboration among six hospitals across five countries, BraTioUS comprises 1669 B-mode 2D ioUS images from 142 glioma patients collected between 2018 and 2023 using various ultrasound systems and acquisition protocols. It also includes masks supervised by experts of tumor segmentation from every ioUS image.

BraTioUS addresses several limitations found in existing public datasets, such as lack of diversity in acquisition hardware, imaging protocols, and glioma types. The primary objective of this dataset is to be publicly available and accessible for the training and validation of machine learning models aimed at improving the interpretation and use of ioUS. The dataset’s scale, quality, and heterogeneity make it a valuable resource for training and validating AI tools aimed at improving intraoperative decision-making and patient outcomes in glioma surgery.

br（脑肿瘤术中超声）数据集[1]是一个大规模的、多中心的、公开的胶质瘤手术中获得的术中超声（iu）图像集合。brious是由五个国家的六家医院通过国际合作创建的，包括2018年至2023年期间使用各种超声系统和采集方案收集的142名胶质瘤患者的1669张b模式2D白条图像。它还包括由专家监督的从每个ioUS图像中分割肿瘤的掩模。br解决了现有公共数据集中存在的几个限制，例如采集硬件、成像协议和胶质瘤类型缺乏多样性。该数据集的主要目标是公开可用，并可用于训练和验证旨在改进借据解释和使用的机器学习模型。该数据集的规模、质量和异质性使其成为训练和验证人工智能工具的宝贵资源，旨在改善胶质瘤手术中的术中决策和患者预后。

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引用次数: 0

Dataset from the Indonesian adaptation of the center for epidemiologic studies depression scale for emerging adults 数据集来自印度尼西亚流行病学研究中心对新兴成人抑郁量表的改编

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief

Pub Date : 2026-01-15 DOI: 10.1016/j.dib.2026.112479

Novia Cuyanto, Esther Widhi Andangsari

This article describes a dataset generated from the Indonesian adaptation of the Center for Epidemiologic Studies Depression Scale (CES-D). The dataset consists of responses collected from 236 emerging adults aged 18–29 years residing in Greater Jakarta, Indonesia, of which 221 valid responses were retained after applying eligibility criteria. The instrument was translated and culturally adapted following international guidelines, including forward-backward translation and expert review to ensure conceptual and linguistic equivalence. The dataset includes responses to 20 self-report items assessing depressive symptoms, demographic information (age, gender, education), and statistical outputs derived from reliability and validity analyses. Confirmatory Factor Analysis (CFA) was conducted with indices including Chi-square (χ²), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Standardized Root Mean Square Residual (SRMR). Reliability measures include Cronbach’s alpha (α) and composite reliability coefficients.

本文描述了流行病学研究中心抑郁量表（CES-D）在印度尼西亚改编后的数据集。该数据集包括从居住在印度尼西亚大雅加达的236名18-29岁的新兴成年人收集的回复，其中221份有效回复在应用资格标准后被保留。该文书的翻译和文化调整是按照国际准则进行的，包括前后翻译和专家审查，以确保概念和语言上的对等。该数据集包括对20个自我报告项目的回答，评估抑郁症状、人口统计信息（年龄、性别、教育程度），以及从信度和效度分析得出的统计结果。采用卡方（χ²）、均方根近似误差（RMSEA）、比较拟合指数（CFI）和标准化均方根残差（SRMR）等指标进行验证性因子分析（CFA）。信度指标包括Cronbach’s alpha （α）和复合信度系数。

引用次数: 0