Data in Brief最新文献

{"title":"Dataset of UAV-acquired thermal and visible images for solar photovoltaic hotspot detection using MLX90640BAB and raspberry Pi 5","authors":"Farhan Ferdous,&nbsp;Bishwajit Banik Pathik,&nbsp;Mushfiqur Rahman,&nbsp;Ahnaf Tahamid,&nbsp;Fauzia Fariha Trona","doi":"10.1016/j.dib.2026.113288","DOIUrl":"10.1016/j.dib.2026.113288","url":null,"abstract":"<div><div>Hotspots are among the most common and damaging thermal anomalies in solar photovoltaic (PV) modules, indicating localized cell defects, shading or soiling that reduce energy yield and can shorten module lifespan [1,2]. However, publicly available thermal image datasets for PV hotspot detection remain scarce, particularly datasets acquired with low-cost thermal sensors in South Asian operating conditions. This article presents a dataset of UAV acquired thermal fusion and visible images of operating rooftop PV modules collected in Dhaka, Bangladesh. Fifty raw samples were captured across two sites: 25 at Mirpur on 06 June 2026 and 25 at Mohammadpur on 22 June 2026. Images were acquired using a custom built quadcopter carrying a Raspberry Pi 5 interfaced with an MLX90640BAB far infrared thermal array sensor which produced color thermal fusion images with on screen maximum temperature readouts and a Raspberry Pi Camera NoIR V2 for visible imagery. The 50 raw samples were expanded to a 5000 image dataset through pre-processing, data augmentation and annotated with bounding boxes for a single Hotspot class (4,626 labeled instances in the training split). A YOLO26s detector trained on the dataset for 95 epochs on an NVIDIA A100 GPU achieved a precision of 0.947, recall of 0.889, [email protected] of 0.936 and [email protected]:0.95 of 0.878 on the validation split which confirms that the dataset supports supervised hotspot detection research. The dataset is suitable for benchmarking object detection algorithms by developing low cost aerial PV inspection pipelines and training edge deployable models for automated PV monitoring.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113288"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149143450","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":"Surveillance camera image dataset for visual classification in criminal investigation research (CCTV cameras)","authors":"Dimitar Rangelov,&nbsp;Nikolay Rangelov,&nbsp;Radoslav Miltchev,&nbsp;Evgeni Genchev","doi":"10.1016/j.dib.2026.113322","DOIUrl":"10.1016/j.dib.2026.113322","url":null,"abstract":"<div><div>This data article describes a curated collection of 1421 images of civilian surveillance and security cameras, assembled to support computer vision research on the visual recognition of camera systems in real-world environments. The images were gathered from freely reusable web sources, open image repositories, and manufacturer product pages, and every image was manually reviewed so that each retained file clearly depicts at least one surveillance camera. The collection covers the most common camera form factors, including dome, pan-tilt-zoom (PTZ), bullet, fixed box, multi-sensor, and consumer IP or doorbell cameras, photographed in outdoor street installations, indoor security setups, pole-mounted and wall-mounted configurations, as well as manufacturer product imagery against plain backgrounds. All files follow a sequential naming convention (camera_&lt;ID&gt;) and are provided in a single directory in JPEG format, in RGB colour space, standardised to a uniform resolution of 640 × 640 pixels. A lightweight annotation file (labels.csv) accompanies the images, recording for each file whether it depicts a camera installed in a real environment (718 in-situ photographs) or a manufacturer-style product image (703 images), together with a per-image camera_type label giving the form factor of the device shown. A documented Python loading script and SHA-256 checksums are distributed with the data. The dataset is publicly available on Zenodo under a CC BY 4.0 licence. It can be reused to train and benchmark image classification and object detection models for automated camera canvassing at crime scenes, and to support research in digital forensics, privacy auditing, urban surveillance mapping, and smart-city applications.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113322"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149201648","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 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":"A field-acquired tea leaf image dataset for disease and pest-damage classification in Bangladesh","authors":"Sakibul Hasan Chowdhury,&nbsp;Md Shohel Arman","doi":"10.1016/j.dib.2026.113356","DOIUrl":"10.1016/j.dib.2026.113356","url":null,"abstract":"<div><div>This data article presents a field-acquired collection of 2500 tea-leaf photographs from four tea-growing locations in Chunarughat, Habiganj, Bangladesh, collected between April 07 and June 23, 2025. The repository contains four image-level categories: Blight (Brown and Gray Blight combined; 555 images), Red Rust (620 images), Helopeltis damage (675 images), and finally Healthy Leaf (650 images). Images were captured under naturally varying illumination, leaf orientation, and background conditions and were retained at their available photographic dimensions. The deposited JPEG images retain their original spatial characteristics, with seven distinct resolutions ranging from 1200 × 1600 to 4608 × 3456 pixels (2411 portrait and 89 landscape images; no square images). The collection was acquired using multiple smartphones and preserves natural field backgrounds rather than standardized laboratory scenes. Manual visual quality screening was used to exclude substantially blurred, unclear, and obvious repeated captures, while no artificial augmentation was applied to the deposited files. The principal contribution of the dataset is the release and documentation of the complete 2500-image field repository for disease- and pest-damage classification; an approximately balanced 2000-image subset was previously used for downstream deep-learning experiments. The complete dataset is openly available through Mendeley Data under a CC BY 4.0 license.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113356"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149253390","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}