Data in Brief最新文献

{"title":"BD-PaveSurface: A multi-weather pavement surface image dataset for crack, pothole, and good pavement condition assessment in Bangladesh","authors":"Durjoy Kumar Dutta,&nbsp;Utsho Kundu,&nbsp;Nakib Aman,&nbsp;Al Arian Ahmad,&nbsp;Mohammed Jan-E-Alam Fouad,&nbsp;Nymur Rahaman Antor","doi":"10.1016/j.dib.2026.113180","DOIUrl":"10.1016/j.dib.2026.113180","url":null,"abstract":"<div><div>This data article describes BD-PaveSurface, a pavement surface image dataset developed for image-level assessment of three pavement surface conditions: crack, pothole, and good pavement surface. The images were collected through systematic field surveys from selected segments of National Highways N5 and N6 in Pabna District, which form part of the Pabna-Dhaka highway route in Bangladesh. Smartphone cameras were used to capture pavement surface images under natural outdoor lighting and real traffic-environment conditions, without interrupting normal traffic movement. Data collection was conducted over an extended period across rainy-season, winter, and summer conditions to capture realistic variation in pavement appearance, illumination, surface moisture, shadows, dust, and texture. The collected images underwent manual screening and image-level labeling. Labeling was conducted by a five-member annotation team, with each image independently reviewed by a second team member. Disagreements were resolved through consensus using predefined class criteria and the dominant-condition rule for mixed crack–pothole cases, followed by independent validation by a Civil Engineering faculty member. After removal of unsuitable samples and label verification, the cleaned raw dataset contained 9000 field images, with 3000 images in each pavement condition class. To support machine-learning-based pavement condition assessment, a processed/augmented subset was prepared from the cleaned images. The preprocessing workflow converted images to RGB format and resized them to 224 × 224 pixels. The cleaned images were split into training, validation, and testing subsets before augmentation, and augmentation operations were then applied only to the training subset. The validation and testing subsets contained only standardized original images and were not augmented. The cleaned raw dataset represents 9000 unique field-acquired pavement images. The processed/augmented subset contains 12,000 derivative image files generated from these field-acquired images, comprising standardized 224 × 224 versions of all 9000 cleaned originals and 3000 newly generated training-only augmented images (1000 per class). Accordingly, the dataset contains 21,000 stored image files in total; however, these files do not represent 21,000 distinct field acquisitions or physical pavement locations. Publicly available Bangladesh-focused pavement image datasets containing both crack and pothole samples remain limited. BD-PaveSurface contributes a field-collected, multi-weather pavement image resource from a South Asian road environment and can be reused for automated pavement condition assessment, crack and pothole recognition, transfer learning, data augmentation studies, computer vision benchmarking, and low-cost camera-based road monitoring.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113180"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148844314","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}

{"title":"Orbscan IIz multimodal corneal topography dataset of anterior axial power maps with structured clinical parameters for unsupervised and self-supervised representation learning","authors":"Ali Mustafa Ali Alshaykha,&nbsp;Qabas A. Hameed,&nbsp;Mohammed Basim Omar","doi":"10.1016/j.dib.2026.113138","DOIUrl":"10.1016/j.dib.2026.113138","url":null,"abstract":"<div><div>We present a multimodal corneal topography dataset comprising 3000 anterior axial power maps acquired from 3000 unique patients using the Orbscan IIz slit-scanning device (Bausch &amp; Lomb) at a single private ophthalmology clinic in Iraq. The examinations were performed during routine clinical practice between 2007 and 2025 using the same diagnostic device throughout the acquisition period. Each image is paired with 19 structured clinical parameter fields extracted from the original device output using an automated OCR-based pipeline. These parameters include simulated keratometry astigmatism (SimK_Astig), maximum and minimum corneal power (MaxK and MinK), zone-specific irregularity indices, mean power, astigmatic power, steep and flat axis orientations across 3 mm and 5 mm optical apertures, corneal pachymetry at the thinnest point with spatial coordinates, anterior chamber depth, white-to-white diameter, pupil diameter, and angle kappa with intercept coordinates. All images were fully anonymized, cropped to the central map region, resized to 1024 × 1024 pixels, and saved in lossless PNG format. Following a quality control pipeline assessing image integrity, metadata completeness, and anatomical plausibility, 2633 image–metadata pairs satisfied the predefined inclusion criteria and constitute the quality-controlled subset characterized in this article. The dataset is released without diagnostic labels by design, positioning it as a foundation for unsupervised, self-supervised, and semi-supervised representation learning in anterior segment analysis. The complete dataset is available on Mendeley Data (DOI: 10.17,632/78wt2nc387.3) under a CC BY-NC 4.0 license.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113138"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148844327","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}

{"title":"An audiovisual dataset for emotion recognition (UTeMo) in Spanish language","authors":"Jorge-Arturo Carrasco-Jiménez,&nbsp;Arturo Téllez-Velázquez,&nbsp;Rosebet Miranda-Luna,&nbsp;Antonio Orantes-Molina,&nbsp;Juan-Pablo García-Vázquez,&nbsp;Raúl Cruz-Barbosa","doi":"10.1016/j.dib.2026.113175","DOIUrl":"10.1016/j.dib.2026.113175","url":null,"abstract":"<div><div>Emotion recognition has had remarkable growth in human-computer communication because emotions play an important role in the daily lives of human beings. This task is challenging because emotions are bodily reactions to external stimuli that differ greatly between people. Societies tend to normalize the response to these stimuli depending on their culture and era. In the literature, there are databases designed for the recognition of emotions in various languages, principally English. Although it is possible to find databases in Spanish within this compendium, none of them are specialized in the variant of Mexican Spanish, and most of them contain low image resolution and audio fidelity, which limit their utility for emotion recognition through deep learning as convolutional networks. For these reasons, this data article introduces UTeMo, a new dataset in Spanish language which includes the six basic emotions based on the Paul Ekman's model (anger, sadness, joy, surprise, fear, and disgust) for facial and voice expressions plus a neutral state. The proposed dataset is qualitatively validated to provide a feasible and reliable basis for emotion recognition using machine and deep learning methods.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113175"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148844328","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}

{"title":"A dataset of major and trace element composition in sediments of the Loire river basin (France), records covering 1778-2022","authors":"Louis Manière,&nbsp;Cécile Grosbois,&nbsp;Elie Dhivert,&nbsp;Leslie Mondamert,&nbsp;Jérôme Labanowski,&nbsp;Marc Desmet,&nbsp;Florentina Moatar,&nbsp;Michel Meybeck","doi":"10.1016/j.dib.2026.113162","DOIUrl":"10.1016/j.dib.2026.113162","url":null,"abstract":"<div><div>This paper introduces a comprehensive database documenting major, metallic, metalloid and rare earth elements in sediments throughout the entire Loire Basin, one of the largest basin of western Europe, flowing to Atlantic ocean. This unique compilation integrates three complementary datasets dealing with sediment collected during 2010-2022 and cores represent concentrations ranging from the mid-19th century up to 2022, riverbank profiles and surface sediments collected in the mid 2010s. The sediment core dataset provides a detailed chronological record and includes sedimentological and geochemical analyses from five cores collected along the Loire River, eight cores from its major tributaries, and one from a neighbouring river (Vilaine). In total, 498 dated samples were analysed for 64 parameters offering insights into long-term environmental changes. To enhance the spatial coverage across the basin, the core-based analyses are supplemented by data from 112 sediment samples recovered in riverbank profiles. These samples were strategically collected in sections of the basin where obtaining sediment cores was not unfeasible. Furthermore, the dataset includes analyses of 91 surface sediment samples, which serve to characterize and locate potential present and past anthropogenic sources of trace elements, thereby providing context for sediment records. This rich and multi-faceted dataset offers a robust spatio-temporal record of contaminants driven by anthropogenic impacts. It is a valuable resource for various environmental research applications. Locally, it has already been successfully utilized to investigate the key drivers of contaminant transfer within the Loire Basin. Globally, the scale and comprehensive nature of this dataset make it particularly well-suited for cross-referencing and comparative studies with other European large-river basins. Currently, the spatial and temporal coverage is limited worldwide. However, this work can contribute to a better understanding of human-induced environmental changes across the continent. Finally, particularly within the European Union context, this dataset also serves an operational purpose. It offers in-depth contextualization for contaminant data collected and archived by stakeholders during regulatory surveys.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113162"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148844860","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}

{"title":"Quasi-static compression test dataset of sinusoidally-corrugated multi-cell PLA+ energy absorbers fabricated by fused deposition modeling (FDM)","authors":"Ahmed Saber,&nbsp;Murat Altin,&nbsp;Erdem Acar,&nbsp;Mehmet Ali Güler","doi":"10.1016/j.dib.2026.113173","DOIUrl":"10.1016/j.dib.2026.113173","url":null,"abstract":"<div><div>This article presents an experimental dataset for additively manufactured, sinusoidally-corrugated multi-cell (SCMC) energy absorbers tested under quasi-static axial compression. Forty SCMC configurations were generated by Latin Hypercube Sampling (LHS) over four geometric design variables, together with a nominal configuration (N-SCMC) and a simple tube without corrugation (STWC) as baseline designs. All specimens were fabricated from PLA+ filament on a Bambu Lab A1 Mini fused deposition modeling (FDM) printer, and the PLA+ mechanical behavior was characterized by uniaxial tensile testing in accordance with <span><span>ASTM D638</span><svg><path></path></svg></span>. The two baseline configurations were tested in triplicate to confirm repeatability. The dataset comprises PLA+ tensile characterization data, geometric design parameters, STL geometry files, axial force–displacement responses, and derived crashworthiness indicators such as the crush force efficiency <span><math><mrow><mo>(</mo><mrow><mi>C</mi><mi>F</mi><mi>E</mi></mrow><mo>)</mo></mrow></math></span>, and specific energy absorption <span><math><mrow><mo>(</mo><mrow><mi>S</mi><mi>E</mi><mi>A</mi></mrow><mo>)</mo></mrow></math></span>, along with sequential deformation images captured at 5 mm displacement intervals. These data support surrogate modeling, machine learning applications, optimization, and validation of numerical models for FDM-printed polymer energy absorbers.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113173"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148778043","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}

{"title":"DAYPSCI: Event-based dataset for anomaly detection through fault injection in PLC-controlled industrial cyber-physical systems","authors":"Juan Vicente Martín-Fraile,&nbsp;Nuño Basurto Hornillos,&nbsp;Jesús Enrique Sierra-García,&nbsp;Álvaro Herrero","doi":"10.1016/j.dib.2026.113146","DOIUrl":"10.1016/j.dib.2026.113146","url":null,"abstract":"<div><div>This article presents DAYPSCI, an event-based dataset generated using an industrial cyber-physical system (CPS) testbed based on a PLC-controlled part marking station with Siemens S7-1200 and S7-1500 devices. The system integrates real industrial hardware with digital twin technologies, enabling controlled and repeatable experiments. Data acquisition follows an event-based logging approach, where only changes in system variables are recorded rather than using fixed sampling rates, and each event is associated with its corresponding inter-event time (Δt), enabling precise temporal characterization of system dynamics. The dataset also includes scan-level identifiers (scan_id) and event ordering (event_order), preserving the logical execution order within PLC scan cycles. It contains time-stamped records of digital sensors, solenoid valve control signals, and process states under both normal operation and controlled fault injection scenarios affecting sensors, actuators, or both. Ground truth is generated through a hybrid approach combining externally defined labels from the experimental configuration and labels derived at runtime from control system signals (e.g., GEMMA states), ensuring a clear separation between CPS execution and the labeling layer while enabling traceability between injected faults (cause) and observable system behavior (effect), and allowing differentiation between fault activation and observable anomaly manifestation, which may be temporally decoupled. The dataset is organized into independent experimental batches, each including processed data (CSV), network traffic captures (PCAPNG) from a Profinet-based industrial communication network, and detailed documentation, facilitating sequence-based analysis and reproducibility. The dataset supports the development and evaluation of machine learning methods for anomaly detection and fault classification in industrial CPS.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113146"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148778116","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}

{"title":"RugbyScope: a transfer-oriented database of rugby union teams and players","authors":"Vincent Labatut,&nbsp;David J.P. O'Sullivan","doi":"10.1016/j.dib.2026.113231","DOIUrl":"10.1016/j.dib.2026.113231","url":null,"abstract":"<div><div>The increasing availability of Web data creates new opportunities for studying the history and organization of sports. However, it also raises major challenges regarding data integration, completeness, and consistency. In this work, we introduce <em>RugbyScope</em>, a comprehensive database of rugby union players, teams, and stints. It was built by aggregating data from two open knowledge bases (Wikidata, DBpedia) and five language editions of Wikipedia, carefully selected to cover all the main rugby union nations. Building such a resource requires addressing several challenges, including heterogeneous data structures across sources, incomplete coverage, and inconsistencies between records describing the same entities. We propose a processing pipeline that extracts data from different sources, formats, and languages, that implements a merging strategy based on data consistency constraints, source agreement, and manually validated heuristics. The resulting database provides a structured and unified representation of rugby union careers, including player information, team histories, and career stints. RugbyScope currently focuses on men's rugby union and achieves a higher coverage of professional clubs worldwide. Although some limitations remain regarding source completeness and historical depth, we provide a reproducible framework and an openly documented methodology for constructing large-scale sports databases from heterogeneous Web sources. RugbyScope is intended as a research resource for studying the evolution, structure, and global dynamics of rugby union.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113231"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148940482","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}

{"title":"A multidimensional cybercrime annotation dataset for a closed-access Russian–English underground hacking forum","authors":"Mariella Mischinger,&nbsp;Sergio Pastrana,&nbsp;Guillermo Suarez-Tangil","doi":"10.1016/j.dib.2026.113171","DOIUrl":"10.1016/j.dib.2026.113171","url":null,"abstract":"<div><div>This dataset provides a multidimensional cybercrime annotation layer for XIN, a closed-access Russian–English underground hacking forum comprising approximately 1.3 million post annotations across roughly 200,000 threads and 52 sub-forums, posted between February 2005 and August 2025. Every post is annotated along four dimensions drawn from established underground-forum coding schemes: post type (the communicative function of the post), author intent (the stance or social purpose), crime type (the category of cybercrime discussed), and supply-chain category (the role of the traded product or service). Labels were assigned through a codebook-based prompting procedure that applied a predefined scheme of category definitions, decision rules, and anonymized examples. We release the codebooks and the per-dimension label distributions over the full corpus. The annotations enable supervised and zero- or few-shot classification, benchmarking of automated labeling against a documented scheme, cyber threat intelligence extraction, and cross-lingual analysis of a community that is underrepresented in public corpora.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113171"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951902","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}

{"title":"A dataset of genuine and synthetic Portuguese speech covering European, Brazilian, African, and regional accents - An open resource for speech deepfake detection and trustworthy AI research","authors":"André Miguel Viegas Oliveira,&nbsp;Rafael Geraldo dos Santos,&nbsp;Helder Daniel,&nbsp;José Valente de Oliveira","doi":"10.1016/j.dib.2026.113214","DOIUrl":"10.1016/j.dib.2026.113214","url":null,"abstract":"<div><div>Portuguese is spoken by approximately 300 million people, making it one of the most widely spoken languages in the world, with speakers distributed across every continent. Despite its global reach, Portuguese remains significantly underrepresented in current synthetic speech detection technologies. This dataset aims to address this critical gap in the existing detection solutions. This dataset may be used in tasks related to biometric security, speaker verification, anti-spoofing systems, and deepfake protection, supporting the development and evaluation of models for Portuguese language speech data.</div><div>The Synthetic Speech Detection - Portuguese (SSD_PT) dataset consists of 242,156 audio samples in the Portuguese language, containing short speech segments ranging from 1 to 7 s each. The dataset is organized into two balanced classes: genuine speech samples (bonafide) and synthetic speech samples (spoof).</div><div>For the Brazilian Portuguese portion of the dataset, the genuine speech samples were collected from publicly available repositories, namely VoxForge, Mozilla Common Voice, and OpenSLR. As no publicly available repositories with comparable coverage were found for the remaining Portuguese varieties, the genuine speech samples for the remaining portions of the dataset were collected from television interviews, radio programs, and podcasts representing different regions of the Lusophone world, including mainland Portugal, the Azores, Madeira, Angola, and Mozambique.</div><div>The synthetic speech samples were generated from genuine speech material. For Brazilian Portuguese, a text-to-speech model based on Coqui TTS (XTTS-v2) was used. For other Portuguese variants, voice conversion techniques were applied using the RVC (Retrieval-based Voice Conversion) framework, requiring the creation of speaker-specific models trained on multiple audio segments to capture individual vocal characteristics.</div><div>All audio files were standardized to a 16 kHz sampling rate and converted to mono format. The dataset is split into training, development, and evaluation subsets, accompanied by protocol files that associate each sample with its corresponding label.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113214"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951936","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}

{"title":"Corrigendum to “Acquisition of balinese imagined spelling using electroencephalogram (BISE) dataset” [Data in Brief, volume 60, June 2025, 111454]","authors":"I Made Agus Wirawan,&nbsp;Ketut Paramarta","doi":"10.1016/j.dib.2026.113263","DOIUrl":"10.1016/j.dib.2026.113263","url":null,"abstract":"","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113263"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148999675","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}