Data in Brief最新文献

{"title":"A robot-view image dataset of photovoltaic panel reference lines with paired synthetic soiling for cleaning robot navigation","authors":"Xin Li,&nbsp;Kumeresan A. Danapalasingam,&nbsp;Norhaliza Abdul Wahab,&nbsp;Trung Dung Ngo,&nbsp;Erwin Van de Put,&nbsp;Jan Ove Rogde Mjånes,&nbsp;Salinda Buyamin,&nbsp;Sivasankar Ganesan","doi":"10.1016/j.dib.2026.113308","DOIUrl":"10.1016/j.dib.2026.113308","url":null,"abstract":"<div><div>This dataset provides robot-view photovoltaic (PV) panel images for reference line perception under visual soiling. The images are captured from a top-down camera viewpoint that approximates the onboard view of a PV cleaning robot. The dataset contains 6200 RGB images at a resolution of <span><math><mrow><mn>160</mn><mspace></mspace><mo>×</mo><mspace></mspace><mn>120</mn></mrow></math></span> pixels, with line-instance annotations for three classes: <em>white cell-gap lines, horizontal</em> and <em>vertical frame-cell border lines</em>. This dataset supports real-time navigation of PV cleaning robots under different soiling levels, including heading angle estimation and the trigger signal for path reversal. The training and validation splits include 4800 images derived from 800 manually annotated clean source images. Each source image is stored together with five synthetic soiling variants, forming six soiling conditions: <em>clean, very-light, light, medium, heavy</em>, and <em>extreme</em>. The images in the <em>very-light, light, medium, heavy</em>, and <em>extreme</em> conditions are synthetically produced by a reproducible augmentation pipeline with six foreground effects: <em>bird-dropping, water-stain, puddle, glare, frame-water</em>, and <em>frame-dust</em>. Additional <em>bird-dropping</em> and <em>glare</em> effects are also sampled in background regions away from labeled reference lines. The test split comprises 1400 images: 1200 images stored in six separate folders, one for each soiling condition, and 200 laboratory real-soiling images of two PV panels manually soiled with pigment droplet, sand, wastewater, clean water, and high-intensity light. Each annotation record stores polar line parameters, line segment endpoints, and a structural-line-class identifier. The dataset is designed as a geometry-centered benchmark rather than a general PV inspection collection. Its paired synthetic protocol keeps the structural labels fixed while increasing visual soiling, enabling controlled robustness analysis without changing scene geometry. The augmentation code is released with the dataset to reproduce the synthetic soiling levels from clean annotated images.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113308"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149202025","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":"Dataset of electric autonomous dial-a-ride instances with local energy communities and electricity tariffs","authors":"José Almeida,&nbsp;Steffen Limmer,&nbsp;João Soares,&nbsp;Maria Bresich,&nbsp;Günther Raidl,&nbsp;Zita Vale","doi":"10.1016/j.dib.2026.113063","DOIUrl":"10.1016/j.dib.2026.113063","url":null,"abstract":"<div><div>Shared autonomous electric vehicle (AEV) fleets offer significant potential for decarbonizing urban mobility, but their efficient operation requires jointly optimizing passenger routing and battery charging under real-world energy pricing constraints. The electric autonomous dial-a-ride problem (e-ADARP) formalizes this challenge, and its integration with local energy communities (LECs) introduces further complexity by coupling fleet scheduling decisions with time-varying community energy dynamics. This article presents a dataset of 20 benchmark instances for the e-ADARP integrated with LECs. The instances are derived from real-world taxi trip data from the city of Porto, Portugal, and incorporate energy data from two clustered LECs, each consisting of 10 prosumers with photovoltaic generation and individual load profiles. Each LEC is associated with a charging station located at the geographic centroid of its prosumers. The dataset comprises instances across 10 size configurations, ranging from 2 AEVs and 20 requests to 20 AEVs and 200 transportation requests, with two electricity tariff scenarios per configuration: a time-of-use tariff and a flat tariff, both incorporating mid-market rate pricing during periods of LEC overproduction. Each instance file encodes geographic coordinates of all nodes, time windows for pickups and dropoffs, vehicle parameters (battery capacity, energy consumption, minimum final state of charge), charging station characteristics, travel time matrices computed via OpenStreetMap shortest paths, and time-discretized energy profiles including community overproduction and electricity tariffs at 15-minute resolution over a 24-hour scheduling horizon. The dataset also includes the full LEC energy data workbook with individual prosumer load profiles, photovoltaic generation profiles, battery storage parameters, and electricity tariffs for both LECs, as well as the instance generation scripts, enabling researchers to develop alternative energy scenarios or modify community configurations. All files are publicly available in Zenodo.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"67 ","pages":"Article 113063"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13396958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148583564","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":"Dataset of genome sequencing and amoxicillin degradation data of Bacillus cereus BT-C2.4 isolated from aquaculture water","authors":"Hong Lat Luu,&nbsp;Nhu Nguyet Phan,&nbsp;Thanh Nhan Lam,&nbsp;Trang Thi Phuong Phan","doi":"10.1016/j.dib.2026.113083","DOIUrl":"10.1016/j.dib.2026.113083","url":null,"abstract":"<div><div>This dataset describes the draft genome sequence and experimental amoxicillin degradation data of <em>B. cereus</em> BT-C2.4, isolated from aquaculture water in Ben Tre province, Vietnam. The genome was sequenced using Illumina technology and assembled into 61 contigs with a total length of approximately 5.44 Mb and GC content of 35%. Genome annotation was performed using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP). The dataset also includes high performance liquid chromatography (HPLC) data measuring residual amoxicillin concentrations over time, showing that amoxicillin was fully degraded within 24 h under the tested conditions. These data may be useful for studies on antimicrobial resistance, antibiotic biodegradation, and environmental microbiology in aquaculture systems.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"67 ","pages":"Article 113083"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13396940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148583692","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":"Complete genome sequence data for Klebsiella pneumoniae superbug strain KAB03 isolated from ready-to-eat vegetable salads in Central Uganda","authors":"Abubaker Kawooya,&nbsp;Chinyere Nkemjika Anyanwu,&nbsp;Emmanuel Eilu,&nbsp;Hussein Kafeero,&nbsp;Adam Ahmed Adam,&nbsp;Mariam Oyedeji Amusa,&nbsp;Danladi Makeri,&nbsp;Martin Odoki,&nbsp;Rasheed Omotayo Adeyemo,&nbsp;Michael Ben Okon,&nbsp;Ismail Abiola Adebayo,&nbsp;Saheed Adekunle Akinola","doi":"10.1016/j.dib.2026.113094","DOIUrl":"10.1016/j.dib.2026.113094","url":null,"abstract":"<div><div>We present whole-genome sequence data for the <em>Klebsiella pneumoniae</em> strain KAB03, isolated from Ready-To-Eat Vegetable Salads in Kampala District, Central Uganda. Genomic DNA was extracted and sequenced using the Illumina platform. The assembled genome is 5,772,016 base pairs long with a GC content of 56.7%. The data set includes annotated genes related to cross-resistance to conventional antibiotics and widely used disinfectants or antiseptics, such as <em>bla</em>_SHV-1, <em>KpnF, FosA6, OmpK37, MdtQ, oqxA</em>, and <em>KpnG</em> / <em>KpnH</em>. The genome sequence and accompanying metadata are available at <span><span>https://www.ncbi.nlm.nih.gov/nuccore/JBWXCM000000000</span><svg><path></path></svg></span>. This data set is suitable in defining a high-risk multidrug-resistance (MDR), public health surveillance, and comparative genomics among <em>K. pneumonia</em> isolates from the environment.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113094"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148646502","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 Sentinel-2 dataset for railway presence classification across metropolitan France","authors":"Lukas Pasold,&nbsp;Luca Eisentraut,&nbsp;Ricardo Buettner","doi":"10.1016/j.dib.2026.113104","DOIUrl":"10.1016/j.dib.2026.113104","url":null,"abstract":"<div><div>This article presents a Sentinel-2 image dataset for railway presence classification across metropolitan France. The collection contains 10,000 true-color RGB PNG chips with equal representation of railway and railway-absent scenes. Each chip has dimensions of 224 × 224 pixels and covers a ground footprint of 512 m × 512 m.</div><div>Railway chip locations were derived from retained OpenStreetMap railway=rail geometries. The railway class was constructed through geographically balanced sampling along the retained network and distance thinning between selected locations. Railway-absent chips were generated in the local surroundings of railway locations. A railway-absent chip was included only when its complete footprint did not intersect retained railway geometries and did not overlap an already accepted chip footprint.</div><div>The image collection was generated from Copernicus Sentinel-2 Level-2A observations acquired by the Sentinel-2 satellite constellation. Bands B04, B03 and B02 were used to create the red, green and blue image channels. Retrieval covered the period from 23 June 2025 to 23 June 2026 and applied a maximum cloud-cover threshold of 10 percent together with least-cloud-cover mosaicking.</div><div>The included files comprise the image collection and a CSV metadata table containing image filenames, class labels and stable candidate identifiers. The dataset provides material for railway-presence classification, comparison of image-classification architectures, spatial evaluation designs, and geospatial representation-learning workflows.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113104"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148646504","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":"JLHI dataset: a joint localization and human-to-human interaction dataset for indoor environments with different layouts","authors":"Yasir Pulikkal,&nbsp;Yin Hoe Ng,&nbsp;Mohammed Abdulazeem Alameen Abdalla,&nbsp;Muhammad Rizwan","doi":"10.1016/j.dib.2026.113106","DOIUrl":"10.1016/j.dib.2026.113106","url":null,"abstract":"<div><div>This paper introduces a Wi-Fi based joint localization and human-to-human interaction recognition dataset across diverse indoor layouts, known as the JLHI dataset. It includes 5 distinct interactions performed by 20 different pairs of subjects at 3 locations in 3 different indoor layouts with varying occlusion levels, ranging from open spaces to highly cluttered environments. Each pair of subjects performed 50 trials for each interaction per position per layout, resulting in a total of 45,000 trials (i.e., 20 pairs of subjects x 5 interactions x 50 trials x 3 locations x 3 layouts). To generate and collect the dataset, NI USRP-2901 software-defined radios operating at 5.2 GHz with a sampling rate of 5 MHz were used. The capture Wi-Fi signals contain channel state information with including amplitude and phase across 64 subcarriers, along with received signal strength indicator values, recorded using a custom CSI logger coded using GNU Radio Companion. Unlike existing Wi-Fi based human activity datasets which predominantly focus on single-person activities or human-to-human activities in a fixed layout or position, our dataset bridges this critical gap by providing a multi-user interaction dataset under diverse spatial positions and occlusion conditions. It enables the design of robust machine learning-based models for joint localization and human-to-human activity recognition in dynamic indoor environments that are resilient to environmental changes. Additionally, the dataset offers a realistic benchmark for evaluating model effectiveness across diverse layouts, spatial positions, and occlusion levels, enabling the development of systems that perform reliably in real-world settings.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113106"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148733266","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":"Whole-genome sequence data of Bacillus tropicus strain MHES12 isolated from the rhizosphere of tomato in a drought-prone ecosystem in Bangladesh","authors":"Md. Nazmul Hannan,&nbsp;Md. Nayeem Hossain,&nbsp;Rifat Islam,&nbsp;Sabbir Khan,&nbsp;Kamrun Nahar,&nbsp;GKM Mustafizur Rahman,&nbsp;Habibul Bari Shozib,&nbsp;Abul Hossain Molla,&nbsp;M. Nazmul Hoque,&nbsp;Md. Manjurul Haque","doi":"10.1016/j.dib.2026.113123","DOIUrl":"10.1016/j.dib.2026.113123","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We report the whole-genome sequence data of &lt;em&gt;Bacillus tropicus&lt;/em&gt; strain MHES12 isolated from the rhizosphere of tomato plants grown in a drought-prone ecosystem in Rajshahi, Bangladesh (24.44,067° N, 88.40,943° E). Genomic DNA was extracted using the GeneJET Genomic DNA Purification Kit, quality-checked by NanoDrop 2000 and Qubit 4.0 Fluorometer (Thermo Fisher Scientific) and sequenced on the Illumina MiSeq platform (2 × 150 bp) using the Nextera DNA Flex Library Preparation Kit. Raw reads were quality-assessed with FastQC v0.11.3, trimmed with Trimmomatic v0.39 (Q30 = 94% R1, 79% R2 raw; 96% and 88% after trimming), and de novo assembled with SPAdes v4.2.0, yielding a draft genome of 5569,334 bp across 40 contigs at 63× total read coverage (52.9× after trimming), with a GC content of 35.1%, N50 value of 838,108 bp and N90 value of 124,199 bp. CheckM2 (Neural-Network specific model) reported 100% completeness and 0.05% contamination. Taxonomic placement was confirmed by FastANI (96.98% to &lt;em&gt;B. tropicus&lt;/em&gt; N24 type strain), skani (97.21%), NCBI ANI (96.90%) and TYGS dDDH (d4 = 72.5%), all above the 95% ANI and 70% dDDH species-delimitation thresholds. An additional publicly available complete &lt;em&gt;Bacillus tropicus&lt;/em&gt; genome (EMB20, GCA_023159405.1; PRJNA742863) from India shares 99.12% FastANI with MHES12, further supporting the species assignment. A maximum-likelihood core-genome phylogeny built from 1119 single-copy core genes across 21 Bacillus genomes places MHES12 in the &lt;em&gt;B. tropicus&lt;/em&gt; clade with 100% SH-aLRT/ultrafast-bootstrap support. Genome annotation using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) v6.11 predicted a total of 5829 genes, comprising 5707 coding sequences, 24 rRNA genes, 93 tRNA genes, 5 ncRNA genes, and 200 pseudogenes. antiSMASH v8.0 identified 10 biosynthetic gene clusters, including siderophore-associated clusters with petrobactin- and bacillibactin-like features, both of which were independently confirmed against reference operons in the Virulence Factor Database (VFDB), as well as ribosomally synthesized and post-translationally modified peptide (RiPP) clusters, a terpene cluster, and a beta-lactone synthetase cluster. As these biosynthetic cluster classes are commonly distributed among members of the &lt;em&gt;B. cereus&lt;/em&gt; group, their identification is presented as a genome-based reference inventory rather than evidence of functional activity or pathogenicity. RAST subsystem analysis, cross-validated using eggNOG-mapper and Prokka, identified putative genomic determinants associated with osmotic and oxidative stress responses, as well as arsenic, chromium, copper, cadmiu&lt;em&gt;m/z&lt;/em&gt;inc/cobalt, manganese, and iron homeostasis. These findings represent predicted genomic potential and should not be interpreted as evidence of phenotypic expression or experimentally verified resistance or stress tolerance. PathogenFinder2 predicted a low probability of human pathogenicity (score = 0.39","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113123"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148733651","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":"Whole genome sequence data of thermotolerant Pseudobacillus badius strain WCB-1 isolated from Kawah Putih Indonesia","authors":"Alam Ahmad Hidayat,&nbsp;Digdo Sudigyo,&nbsp;Rudi Nirwantono,&nbsp;Joko Pebrianto Trinugroho,&nbsp;Fina Amreta Laksmi,&nbsp;Isa Nuryana","doi":"10.1016/j.dib.2026.113093","DOIUrl":"10.1016/j.dib.2026.113093","url":null,"abstract":"<div><div>The whole genome sequence data of a thermotolerant bacterium, <em>Pseudobacillus badius</em> strain WCB-1, isolated from acidic hot spring sediment at Kawah Putih, West Java, Indonesia. The isolate was obtained using culture-based methods and initially identified by 16S rRNA gene sequencing, followed by a whole genome-based taxonomic analysis. Whole-genome sequencing was performed using a hybrid approach combining Illumina NextSeq 500 and Oxford Nanopore GridION platforms. The assembled genome has a total size of 4,093,976 bp with a GC content of 44.20%, comprising 4,106 protein-coding sequences, 102 tRNAs, and 33 rRNAs. Comparative analysis using Average Nucleotide Identity (ANI) and digital DNA–DNA hybridization (dDDH) confirmed the taxonomic assignment of the new isolate as <em>Pseudobacillus badius</em>. Genome annotation provided a set of predicted genes and functional assignments, and genome mining identified several putative biosynthetic gene clusters. The dataset provides genomic information for a thermotolerant bacterium isolated from acidic hot-spring sediment and can be used for comparative genomics and further functional analyses.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113093"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148733654","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 “Thermomechanical characterisation data of 30 g/m2 and 150 g/m2 cured unidirectional carbon/epoxy tape prepreg TP 402/T700S” [Data in Brief, volume 47, April 2023, 108966]","authors":"Serafín Sánchez-Carmona,&nbsp;María Luisa Velasco,&nbsp;Alberto Barroso,&nbsp;Elena Correa","doi":"10.1016/j.dib.2026.113238","DOIUrl":"10.1016/j.dib.2026.113238","url":null,"abstract":"","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113238"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148949314","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":"Morphologically tagged lexical dataset of Khorezm Dialect and Standard Uzbek Pairs","authors":"Maksud Sharipov,&nbsp;Lola Kurbanova,&nbsp;Ruzikajon Kurbonova,&nbsp;Elmurod Kuriyozov","doi":"10.1016/j.dib.2026.113221","DOIUrl":"10.1016/j.dib.2026.113221","url":null,"abstract":"<div><div>In this article, we present a morphologically annotated lexical dataset designed to support the translation of texts between the Khorezm dialect of Uzbek and standard Uzbek. The database consists of 1445 Khorezm dialect–standard Uzbek word pairs. For each entry, the dialect form, its corresponding standard form, and part of speech are provided, along with a morphological structure segmented into affixes according to such grammatical categories as number (singular/plural), possessive, and person/number properties. During the tagging process, the grammatical system of Uzbek and the specific inflectional properties of the Khorezm dialect were taken into account, resulting in a clear and machine-processable layer of correspondences between the dialect and the standard language. The resulting lexical resource is intended to serve as additional input data for AI-based translation and normalization models, as well as in applications for morphological analysis, spelling and grammar checking, and educational tools for teaching the Khorezm dialect. This dataset constitutes the first systematic lexical-morphological resource for corpus-based research on the Khorezm dialect and lays the groundwork for studies on machine translation and automatic alignment between dialects and the standard language in low-resource Turkic varieties.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113221"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148951946","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}