Data in Brief最新文献

{"title":"A step toward inclusion: A transparent dataset for automatic text simplification for screen reader users","authors":"Nelson Pérez-Rojas,&nbsp;Martín Solís,&nbsp;Saúl Calderón-Ramírez,&nbsp;Mónica Arias-Monge,&nbsp;Horacio Saggion,&nbsp;Danny Xie-Li","doi":"10.1016/j.dib.2026.113217","DOIUrl":"10.1016/j.dib.2026.113217","url":null,"abstract":"<div><div>GeoSimp is a Spanish-language dataset for automatic text simplification (ATS), comprising approximately 3200 discourse segments derived from peer-reviewed geological scientific articles. The dataset was constructed to address two documented gaps in existing ATS resources: the limited availability of Spanish-language corpora for text simplification and text complexity classification, and the scarcity of domain-specific datasets focused on specialized scientific writing.</div><div>Source texts were drawn exclusively from articles published in the Revista Geológica de América Central, a peer-reviewed, open-access journal, all of which are distributed under a single Creative Commons Attribution–NonCommercial–ShareAlike 3.0 (CC BY-NC-SA 3.0) license. Text extraction was performed manually to preserve structural coherence. Paragraphs were copied into Microsoft Excel spreadsheets, assigned unique identifiers, and subsequently segmented into discourse segments by one of the authors, a researcher with expertise in linguistics. Four annotators with university degrees in Spanish philology and professional experience in text editing performed attribute identification and simplification, using a predefined scheme of 21 linguistic complexity attributes. Prior to annotation, these annotators completed a structured training and calibration phase.</div><div>The dataset is distributed as two Excel files. The training file (GeoSimp_train.xlsx) contains 2956 records, each comprising the original discourse segment, one simplified version, the annotator identifier, the identified complexity attributes, the paragraph identifier, and the identifier of the source article. The test file (GeoSimp_test.xlsx) contains 301 records, each comprising the original discourse segment and four independently produced simplified versions with their corresponding attribute annotations. A simplification guidelines manual used during annotation is also included in the repository.</div><div>GeoSimp is suitable for training and evaluating ATS models, for developing text complexity classification models, and for research on paragraph-level simplification. The dataset is specifically oriented toward simplification for blind and low-vision users who rely on screen readers, a population underrepresented in the ATS literature. The explicit annotation of linguistic complexity attributes per segment supports reproducibility and fine-grained error analysis. The dataset is openly available in a public GitHub repository.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113217"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148999681","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 labeled RF signal dataset for UAV detection and classification in the 2.4GHz band under Wifi and Bluetooth coexistence","authors":"Saber Mgannem,&nbsp;Radhoine Aloui,&nbsp;Bilel Hamdi,&nbsp;Sofien Mhatli,&nbsp;Ignacio Llamas-Garro","doi":"10.1016/j.dib.2026.113127","DOIUrl":"10.1016/j.dib.2026.113127","url":null,"abstract":"<div><div>This article presents a labeled radio frequency (RF) dataset for the detection and classification of unmanned aerial vehicles (UAVs) operating in the 2.4 GHz industrial, scientific, and medical (ISM) band. The dataset was acquired using a USRP B210 software-defined radio equipped with bidirectional antennas in an indoor and semi-controlled wireless environment containing realistic WiFi and Bluetooth interference. Data acquisition was performed under multiple operational scenarios, including drone idle, take-off, hovering, movement, and absence of UAV activity. The collected RF signals were transformed into time–frequency spectrogram images using the Short-Time Fourier Transform (STFT) to support machine learning and deep learning applications [1]</div><div>The dataset was annotated using LabelImg [2] in a YOLO-compatible format to support object detection and classification tasks. This resource is intended for researchers working on RF-based drone detection, wireless spectrum monitoring [3], and deep learning-based signal classification. The dataset enables benchmarking under realistic interference conditions and supports reproducible research in UAV detection systems.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113127"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148778044","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 multi-segment underwater acoustic array telemetry dataset with depth, heading, pitch, and roll time series","authors":"Fujin Huang,&nbsp;Lei Zhao","doi":"10.1016/j.dib.2026.113143","DOIUrl":"10.1016/j.dib.2026.113143","url":null,"abstract":"<div><div>This data article presents time-series telemetry datasets collected from underwater acoustic sensor arrays during field deployments. The data comprise synchronized measurements of depth (m), heading (°), pitch (°), and roll (°), recorded at a nominal sampling rate of approximately 1 Hz and provided in comma-separated value (CSV) format with time-of-day timestamps and deployment location identifiers. The release includes two related CSV files covering different array configurations and water-depth conditions, enabling comparative analysis of array geometry dynamics, sensor orientation stability, and depth variation patterns. An accompanying browser-based 3D visualization tool supports rapid data validation and interactive exploration without specialized software. These datasets are intended for researchers in underwater instrumentation, ocean engineering, and geophysical array deployment who require reference telemetry for array behavior analysis, algorithm development, or educational purposes. The data and visualization tool are deposited in a public repository under an open license, with documented file structure and column definitions.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113143"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148778114","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 data collection of UML diagrams in PlantUML notation mined from open-source software","authors":"Volodymyr Polishchuk,&nbsp;Vladyslava Skidan","doi":"10.1016/j.dib.2026.113156","DOIUrl":"10.1016/j.dib.2026.113156","url":null,"abstract":"<div><div>Unified Modeling Language (UML) diagrams are widely used in software engineering education and practice. However, research on real-world UML usage has been constrained by the limited availability of large-scale, openly accessible, and structurally annotated diagram collections, with existing resources differing in size, format, and the absence of per-diagram type labels.</div><div>To address this gap, this data article presents a collection of 143,427 UML diagrams in PlantUML notation, mined from open-source software repositories indexed by the World of Code (WoC) infrastructure. The data pipeline identifies PlantUML files by file extension across World of Code, validates the presence of PlantUML content markers, and compiles the validated files with PlantUML to confirm syntactic correctness.</div><div>Each diagram is then assigned to one of nine standard UML types using a large language model (LLM)-based classifier, and processed by a parser-based tool that extracts per-type counts of 57 structural element kinds and 28 connection kinds. The collection comprises class (51.6%), sequence (24.6%), activity (6.8%), component (5.7%), use case (5.0%), state (3.0%), object (1.7%), deployment (1.4%), and timing (0.1%) diagrams. Each diagram is released as both PlantUML source code and a rendered Portable Network Graphics (PNG) image, accompanied by metadata that records the assigned diagram type, the structural element and connection counts, textual size metrics, and source repository attribution.</div><div>The collection is published on Zenodo. Anticipated reuse scenarios include empirical analyses of UML usage in open-source software, training and evaluation of artificial-intelligence models for diagram understanding and generation, benchmarking of diagram-to-code and code-to-diagram translation tools, and the development of educational resources for software engineering.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113156"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148778110","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":"Gridded sea surface hydrographic data along two Salish Sea passenger ferry routes, 2001–2025","authors":"Ian T. Black,&nbsp;Alice O.V. Bui,&nbsp;Bennit L. Mueller,&nbsp;Kohen W. Bauer","doi":"10.1016/j.dib.2026.113279","DOIUrl":"10.1016/j.dib.2026.113279","url":null,"abstract":"<div><div>Since 2001, several passenger ferries connecting Vancouver Island and mainland British Columbia, Canada have collected sea surface hydrographic data within the Salish Sea. Originally part of two separate monitoring projects in the early 2000s and now maintained by Ocean Networks Canada (ONC), archived and real-time data from these vessels are open to the public. To date, several vessels with scientific underway systems have collected data between Tsawwassen-Nanaimo (TWS-NNO, ∼70 km) and Tsawwassen-Swartz Bay (TWS-SWB, ∼44 km), sometimes up to 8 times per day. This data article describes how the original time-series were obtained, the performance of additional quality assessment, and the steps for creating curated datasets consisting of spatially binned (0.01°) transits of surface ocean water properties in the Salish Sea between 2001–2025. During this period, 30,000+ transits along both routes collected sea surface temperature, practical salinity, and dissolved oxygen; providing dense coverage for a variety of inter-seasonal and inter-annual analyses. Pathways for uncertainty estimation and validation are discussed. Python 3 example code is provided for compiling and processing new data collected after 2025 to enable users to extend this dataset for future analyses.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113279"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149156483","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":"Industrial LiFePO₄ battery management system dataset for early degradation analysis and remaining useful life prediction","authors":"MST. Asfia Binte Ulfat,&nbsp;Afsana Begum,&nbsp;Md. Abdulla Al Mamun,&nbsp;Sawdagar Noor Asfuq E Azam,&nbsp;Md. Mahedi Hassan,&nbsp;Imran Mahmud,&nbsp;A.H.M. Saifullah Sadi,&nbsp;Md. Shohel Sayeed","doi":"10.1016/j.dib.2026.113252","DOIUrl":"10.1016/j.dib.2026.113252","url":null,"abstract":"<div><div>This data article describes an experiment-informed R&amp;D dataset associated with an industrial LiFePO₄ battery pack with a nominal voltage of 25.6 V and a rated capacity of 40 Ah. The dataset was prepared with industrial support from GMP Lithium Ltd., Bangladesh, and represents battery operational behaviour and capacity degradation within practical industrial operating ranges. Capacity(Ah) follows a deterministic degradation trajectory calibrated to the manufacturer’s ageing-test end points, whereas the pack-level operating variables and the eight cell voltages are structured (simulated) cycle-level values generated within the manufacturer’s specification limits; the complete generating equations are reported. The dataset consists of 3000 sequential charge-discharge cycle records in CSV format. Each record contains pack identification, cycle number, battery capacity, State of Health, Remaining Useful Life, pack voltage, pack current, a normalized lifecycle progress indicator labelled DoD(%),<span><span>*</span></span> operating temperature, and individual voltage measurements from eight series-connected cells. The dataset contains 17 variables, including indicators of battery degradation, pack-level operating conditions, and cell-level voltage behaviour. Supporting documentation, including a codebook, README file, and data publication permission letter, is provided with the dataset in the Mendeley Data repository. The dataset can be reused for battery degradation analysis, State of Health estimation, Remaining Useful Life prediction, assessment of cell-voltage imbalance, feature analysis, and development and evaluation of machine learning and deep learning models. The combination of capacity, operational, and cell-level voltage variables enables the study of battery ageing at both pack and cell level. It is not a raw continuous BMS log and was not sampled directly from a physical test bench; it should not be interpreted as such.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113252"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149054165","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":"Draft genome sequence dataset and genomic features of Oceanimonas baumannii strain ICA-29, a shrimp-associated bacterium isolated from a commercial aquaculture system","authors":"Guillermo Reyes,&nbsp;Martha Borbor,&nbsp;Ramiro Solórzano,&nbsp;Juan Carlos Muñoz,&nbsp;Rosa Malavé,&nbsp;Betsy Andrade,&nbsp;Bonny Bayot","doi":"10.1016/j.dib.2026.113280","DOIUrl":"10.1016/j.dib.2026.113280","url":null,"abstract":"<div><div>Bacterial diseases represent a major challenge for <em>Penaeus vannamei</em> shrimp aquaculture. Here, we report the draft genome sequence and genomic characterization of <em>Oceanimonas baumannii</em> strain ICA-29, isolated from juvenile shrimp collected in a commercial pond affected by acute hepatopancreatic necrosis disease (AHPND). Illumina sequencing yielded a 3.58 Mb draft genome assembled into 42 contigs, with a 55.94% G+C content and 99.21% completeness. Taxonomic assignment was supported by phylogenomic analysis, an average nucleotide identity (ANI) value of 99.22%, and a digital DNA-DNA hybridization (dDDH) value of 93.50% relative to the reference <em>O. baumannii</em> strain DSM 15594. Genome annotation identified 3,360 coding sequences (CDSs). COG functional profiling revealed a predominance of metabolism-related functions, followed by cellular processes and signaling, and information storage and processing. Screening against antimicrobial resistance and virulence factor databases identified only low-confidence homology matches to antimicrobial resistance-associated entries and several putative homologs to entries contained in the Virulence Factor Database (VFDB), primarily related to conserved polar and lateral flagellar motility functions. These data provide a genomic resource for comparative analyses of <em>Oceanimonas</em> species and for the investigation of bacterial traits relevant to aquaculture systems.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113280"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149143456","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":"ABRLog: a multi-device android bugreport log dataset for intelligent analysis","authors":"Ricardo Miranda Filho,&nbsp;João Alfredo Bessa,&nbsp;Rosiane de Freitas","doi":"10.1016/j.dib.2026.113230","DOIUrl":"10.1016/j.dib.2026.113230","url":null,"abstract":"<div><div>Android devices generate a wealth of diagnostic data through their built-in logging subsystems, offering invaluable insights into application behavior, hardware performance, and operating system stability. However, the effective utilization of this data for intelligent analysis — particularly, for anomaly detection, failure diagnosis, and security monitoring — remains constrained by the scarcity of publicly available, well-structured, and multi-device log datasets. To address this gap, we introduce ABRLog (Android BugReport Log), a comprehensive multi-device log dataset derived from bug report captures collected across 15 distinct Android devices representing five major manufacturers (Samsung, Motorola, Google, Xiaomi, and Huawei) and spanning six Android versions, from 9 to 14. A total of 45 bugreport archives were systematically processed through two complementary extraction strategies: (i) line-by-line regular expression matching for granular event extraction; and (ii) section-based delimiter parsing for high-level block segmentation. This dual approach yielded 1012 structured log files, organized into 24 semantically coherent block types. The final dataset comprises approximately 10 million log lines (1.14 GiB), extracted from 6.34 GiB of raw bugreport data. By providing a systematically curated, multi-vendor, multi-version log corpus, ABRLog aims to serve as a foundational resource for the development and benchmarking of machine learning models for log-based anomaly detection, predictive maintenance, and intelligent system diagnostics on Android platforms.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113230"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149045888","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":"Experimental data from an open-source DC motor control educational kit for calibration, system identification, and controller validation","authors":"Alejandro Von Chong,&nbsp;Dorindo Cárdenas","doi":"10.1016/j.dib.2026.113274","DOIUrl":"10.1016/j.dib.2026.113274","url":null,"abstract":"<div><div>This data article presents raw and processed experimental data collected with the OpenMCT DC motor control educational kit. The dataset includes current-sense calibration data, static motor characterization data, system-identification logs at four sampling times, continuous PI controller validation at four loop periods, discrete controller validation for three controller designs, and an additional chirp-excitation characterization run. The raw acquisition files were exported from the OpenMCT Python/Qt graphical user interface as comma-separated text files containing reference commands, measured speed, loop interval, raw and filtered current-sense values, PWM command, and synchronized digital multimeter measurements. MATLAB scripts, processed files, model tables, screenshots, and exported figures are provided to reproduce the data organization and plots. The records can be used to prepare laboratory exercises in which students identify a motor model and compare its predicted response with experimental measurements. They also provide reference data for evaluating alternative identification and controller-validation methods.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113274"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149045889","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":"Concrete400: A multi-task pixel-level crack dataset from urban infrastructure in Vietnam","authors":"Le Hoai Nam,&nbsp;Nguyen Ngoc Lan,&nbsp;Vu Manh Trung,&nbsp;Nguyen Nam Son,&nbsp;Vu Quang Tuyen,&nbsp;Tran Ngoc Hoa","doi":"10.1016/j.dib.2026.113246","DOIUrl":"10.1016/j.dib.2026.113246","url":null,"abstract":"<div><div>Concrete400 is a multi-task concrete crack dataset for visual infrastructure inspection. It contains 379 RGB photographs of building surfaces and concrete pavements in Hanoi, Vietnam, with manually prepared pixel-level crack masks and corresponding text files containing crack region bounding box annotations. The release also includes 1502 labelled image patches for binary crack classification, comprising 633 crack patches and 869 no-crack patches. The collection preserves field variability in illumination, concrete texture, surface condition, background clutter, and acquisition time. The released segmentation and object detection images, masks, and box coordinates are standardised to 512 <span><math><mo>×</mo></math></span> 512 pixels, while the classification patches are provided at 256 <span><math><mo>×</mo></math></span> 256 pixels. The accompanying reproducible protocol defines 303 training and 76 validation image–mask pairs. After masks are binarised at an intensity threshold of 127, crack pixels represent 3.08% of image pixels on average. Concrete400 can therefore support patch-level crack classification, semantic crack segmentation, instance-style crack region segmentation, object detection, semi-supervised and self-supervised learning, and crack image generation under realistic inspection conditions.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113246"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149045947","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}