Data in Brief最新文献

{"title":"Single-nucleus transcriptomic data of susceptible and resistant tobacco stems after black shank infection","authors":"Xiaolin Lu,&nbsp;Zhijun Tong,&nbsp;Canhua Lu,&nbsp;Chen Tan,&nbsp;Hongyu Chen,&nbsp;Longjiang Fan,&nbsp;Bingguang Xiao","doi":"10.1016/j.dib.2026.113095","DOIUrl":"10.1016/j.dib.2026.113095","url":null,"abstract":"<div><div>Black shank, caused by <em>Phytophthora nicotianae</em>, is a major disease in tobacco production. Previous transcriptomic work has used <em>Nicotiana tabacum</em> L. cv. Honghuadajinyuan (HD) as a susceptible tobacco material in black shank research. Presented here is a single-nucleus RNA sequencing (snRNA-seq) dataset generated from stem tissues of two tobacco accessions with contrasting responses to black shank, namely the susceptible cultivar HD and the black shank-resistant near-isogenic line HDR7 derived from HD. Stem tissues surrounding the inoculation site were collected at 48 h post inoculation with <em>P. nicotianae</em> race 0 and used for nucleus isolation, library construction, and sequencing. Because plant tissues are often difficult to dissociate into intact single cells, nucleus-based transcriptomic profiling provides a practical way to generate cell-resolved expression data from these samples. The dataset contains snRNA profiles from infected tobacco stems of both susceptible and resistant materials and can be reused for cell-type annotation, marker screening, comparative analysis of transcriptional features between accessions, and integration with future tobacco stem or disease-related single-cell and single-nucleus transcriptomic datasets.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113095"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148733652","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":"Tissue-specific lead (Pb) partitioning dataset for sweetpotato cultivars ‘Bayou Belle’ and ‘Beauregard’ under controlled greenhouse Pb exposure","authors":"Arthur Q. Villordon,&nbsp;Mae Ann Bravo,&nbsp;Marissa Barbosa,&nbsp;Don LaBonte,&nbsp;Lorin Harvey","doi":"10.1016/j.dib.2026.113114","DOIUrl":"10.1016/j.dib.2026.113114","url":null,"abstract":"<div><div>This data article describes a greenhouse-derived dataset of lead (Pb) concentrations in four tissue types from two orange-fleshed sweetpotato cultivars, ‘Bayou Belle’ and ‘Beauregard’, that were exposed to controlled Pb treatments. Plants were grown from May 7 to June 28, 2026, and assigned to three treatment levels: untreated control, 10 mg L⁻¹ Pb, and 20 mg L⁻¹ Pb. Pooled tissue samples were collected from storage root, lateral and non-swollen root tissue, stem, and leaf, with three biological replicates per cultivar × treatment × tissue combination; each biological replicate consisted of tissue pooled from three subsamples to meet the minimum sample-weight requirement for elemental analysis. The dataset contains 72 observations with variables for cultivar, Pb treatment, tissue source, Pb concentration, and sample weight. Lead concentrations are reported in μg kg⁻¹ and were measured in digested plant tissue using inductively coupled plasma mass spectrometry. The repository includes the analysis-ready CSV file, two R scripts for weighted least squares and unweighted sensitivity workflows, and README documentation. These data can support tissue-level Pb partitioning research, cultivar comparisons, food safety assessment, statistical workflow testing, and sweetpotato sampling protocol development.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113114"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148733653","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":"COVID-19 advanced cardiovascular and clinical phenotyping dataset","authors":"Oscar E. Barreras,&nbsp;Daniel Cuevas-González,&nbsp;Marco A. Reyna,&nbsp;Edgar García-Duarte,&nbsp;Juan Pablo García-Vázquez,&nbsp;Juan C. Delgado-Torres,&nbsp;Martin Aaron Sánchez-Barajas,&nbsp;Roberto L. Avitia","doi":"10.1016/j.dib.2026.113139","DOIUrl":"10.1016/j.dib.2026.113139","url":null,"abstract":"<div><div>This article describes a structured clinical dataset collected from 100 adult patients who were previously hospitalized due to moderate-to-severe COVID-19 infection. Data were obtained prospectively at a second-level care hospital in Mexicali, Baja California, Mexico. The dataset includes demographic characteristics, anthropometric measurements, traditional cardiovascular risk factors, general blood test, Questionnaire-derived variables, 12-lead electrocardiographic (ECG) parameters, signal-averaged electrocardiogram (SAECG) measurements, and heart rate variability (HRV) metrics. Questionnaire-derived variables include medical history, cardiovascular risk factors, medication use, lifestyle-related factors, and COVID-19 vaccination information. Laboratory variables include metabolic, renal, lipid, electrolyte, and hematological parameters obtained from routine clinical blood analyses. The 12-lead ECG data include cardiac rhythm classification, measurements of wave and interval durations, electrical axis parameters, and precordial voltage amplitudes. SAECG variables are provided as raw numerical measurements, including total QRS duration, low-amplitude signal duration below 40 µV (LAS40), and root mean square voltage of the last 40 ms (RMS40), together with a variable indicating the presence or absence of ventricular late potentials. HRV parameters are available as continuous time-domain and frequency-domain values, allowing future researchers to apply different clinical thresholds or computational approaches. The dataset is organized into multiple structured Comma-Separated Values (CSV) files distributed across dedicated subdirectories within the Mendeley Data repository. The repository also includes supporting PDF documentation, such as the ethics approval document containing the protocol number, the questionnaire applied during data collection, and the informed consent form. An overview of each variable is provided, and only anonymized patient identifiers are included; no personally identifiable information is contained in the dataset. These data may support future exploratory studies on cardiovascular alterations in post-COVID populations and machine learning applications. The structured format is intended to facilitate secondary data analyses, such as correlational studies and the identification of associations between clinical and electrocardiographic variables.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113139"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148733977","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":"BiCervi: Pap smear microscopy image dataset for cervical cancer detection and classification","authors":"Hope Mbelwa,&nbsp;Judith Leo,&nbsp;Crispin Kahesa,&nbsp;Asafu Munema,&nbsp;Elizabeth Mkoba","doi":"10.1016/j.dib.2026.113077","DOIUrl":"10.1016/j.dib.2026.113077","url":null,"abstract":"<div><div>Cervical cancer is still a major public-health challenge, especially in low and middle-income countries where there are limited numbers of specialists to review Pap smears. While Pap smear screening is widely used, manual reading is slow and can vary from one expert to another, which makes data-driven decision support increasingly important. However, Pap smear imagery datasets from African screening settings are still limited, slowing down the development and fair evaluation of machine-learning models. This article presents a curated dataset of Pap smear microscopy images collected at The Ocean Road Cancer Institute (ORCI) in Tanzania. Images were obtained using a digital microscope with a 10 × /0.25 objective lens, and stored as RGB JPEG images at a resolution of a 2592 × 1944 pixels. The Bethesda System for reporting cervical cytology was used to label the images and organize them into eight diagnostic categories: Atypical Squamous Cells of Undetermined Significance (ASC-US), Atypical Squamous Cells, cannot exclude High-grade Squamous Intraepithelial Lesion (ASC-H), Atypical Glandular Cells (AGC), High-grade Squamous Intraepithelial Lesion (HSIL), Low-grade Squamous Intraepithelial Lesion (LSIL), Negative for Intraepithelial Lesion or Malignancy (NILM), Squamous Cell Carcinoma, and Adenocarcinoma. Labels were independently reviewed by two experts for consistency, and disagreements were resolved through adjudication to reach a final decision; images that could not be labeled with sufficient confidence were excluded during quality control. The resulting dataset comprises 3,000 images and can be used to develop Artificial Intelligence-based tools for cervical cancer detection.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113077"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13417976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148618704","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":"Data of annotated draft genome assemblies of three bacterial isolates from the ‘Panchabrihi’ rice, soybean, and bean-associated niches in Bangladesh","authors":"Nusrat Sultana,&nbsp;Abed Chaudhury,&nbsp;Saria Farheen","doi":"10.1016/j.dib.2026.113081","DOIUrl":"10.1016/j.dib.2026.113081","url":null,"abstract":"<div><div>We have collected the bacterial communities inhabiting the rhizosphere and seed-surface of selected crop plants (‘Panchabrihi’ rice, soybean, and bean) in Bangladesh and created a draft genome assembly database of the isolated bacterial strains. Three bacterial isolates were : A1 from the rhizosphere of Panchabrihi rice, A7 and A10 from the surface of soybean and bean seeds. Whole-genome sequencing, draft genome assembly, BLAST comparison with reference genomes, Nucleotide Identity (ANI), digital DNA-DNA hybridization (dDDH), Average Amino Acid Identity (AAI), phylogenomic analyses, and annotations using Bakta revealed that the isolates are <em>Bacillus cereus</em> A1, <em>Bacillus subtilis</em> A7, and <em>Acinetobacter soli</em> A10. Paired-end Illumina sequencing data, draft genome assembly, and annotations generated were deposited in the NCBI database under BioProject PRJNA1366070. Afterwards, we have analysed the potential of plant growth–promoting (PGP) capacities using the eggNOG-mapper analysis, COG functional classification, secondary metabolite biosynthetic gene cluster analysis using antiSMASH, and KEGG Orthology (KO) completeness analysis. After manual curation of the functional properties of the predicted genes, we found that all three genomes were associated with PGP functions, including nutrient solubilization, phytohormone production, stress tolerance, and root colonization. Additionally, we have also analysed the antibiotic resistance genes (ARGs), mobile genetic elements, virulence-associated genes, toxin genes, and pathogenicity genes of the isolated strains, and generated the respective database. The draft genomes were also found to have several antibiotic resistance determinant genes, mobile genetic elements, virulence-associated genes, and human pathogenic genes. Therefore, we have performed a comparative biosafety risk assessment on all three genomes and concluded that <em>Bacillus subtilis</em> A7 (rather than the A1 and A10) will be a safer option for possible sustainable agricultural practices. We have generated a comprehensive metadata table and submitted all relevant databases to Zenodo. Our databases will be useful for future identification of the potential PGP bacterial strains and for assessing the biosafety traits before agricultural application.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113081"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148646495","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":"BrajText-Saar: A structured cultural dataset for cultural text mining in indian heritage texts","authors":"Mukta M. Deshpande,&nbsp;Prafulla B. Bafna","doi":"10.1016/j.dib.2026.113086","DOIUrl":"10.1016/j.dib.2026.113086","url":null,"abstract":"<div><div>India is one of the countries where many regional languages are spoken in different parts of the country. Each part of the country has its unique cultural traditions; therefore, an abundance of cultural texts is available in Indian Regional languages. The cultural texts are mostly ancient scriptures that represent the community's heritage, values, and ancient knowledge. Braj is one of the Indian regional languages that represents the cultural heritage of the Braj region. The BrajText-Saar dataset contains texts from the Braj language, which features a variety of prehistoric scripts. The data was collected from the Maan Mandir trust's portal, which operates to maintain Braj culture and heritage. The Braj text primarily explains the divine actions and the life of Lord Krishna in the form of poetry and devotional songs. Offline Braj literature from renowned writers is available in manuscript form. This represents an opportunity for cultural text mining in the Braj Language. The developed dataset opens new paths in various research areas, including language studies, sentiment analysis, and emotion analysis, and is also suitable for digital humanities research.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113086"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148646902","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 chloroplast genome sequence data of Camellia handelii Sealy (Theaceae)","authors":"Zhun Xiao,&nbsp;Jinrui Yan","doi":"10.1016/j.dib.2026.113144","DOIUrl":"10.1016/j.dib.2026.113144","url":null,"abstract":"<div><div>This data article describes the complete chloroplast genome sequence data of <em>Camellia handelii</em> Sealy, a fragrant-flowered species of <em>Camellia</em> sect. <em>Theopsis</em>. Fresh young leaves were collected from a cultivated individual in Changsha, China. Genomic DNA was extracted and sequenced on the Illumina NovaSeq platform to generate 100 bp paired-end reads. Raw reads were processed using fastp, and chloroplast-derived reads were enriched by mapping to the reference chloroplast genome of <em>Camellia sinensis</em>. The chloroplast genome was assembled using GetOrganelle and annotated using GeSeq.</div><div>The complete chloroplast genome of <em>C. handelii</em> is 156,901 bp in length and has a typical quadripartite structure, including an LSC region of 86,505 bp, an SSC region of 18,268 bp, and two IR regions of 26,064 bp each. The genome has an overall GC content of 37.33% and contains 132 annotated genes. A phylogenetic dataset based on 72 concatenated orthologous single-copy chloroplast genes from 44 taxa was also generated. The genome sequence, raw sequencing reads, and phylogenetic dataset are available under GenBank accession PZ504125, BioProject PRJNA1476376, BioSample SAMN60712287, SRA accession SRR39069759, and Figshare DOI <span><span>https://doi.org/10.6084/m9.figshare.32625783</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113144"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148778060","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":"Validated and enriched Android application dataset: integration of VirusTotal and Quark-Engine intelligence","authors":"Fadi Mohsen,&nbsp;Yulin Zhang,&nbsp;Ali Murat Gültekin,&nbsp;Halil Bisgin,&nbsp;Koerner Buchta","doi":"10.1016/j.dib.2026.113152","DOIUrl":"10.1016/j.dib.2026.113152","url":null,"abstract":"<div><div>This data article describes a large-scale Android application dataset built by enriching a previously released Google Play removal corpus with external malware intelligence from VirusTotal Premium v3 Enterprise and Quark-Engine 25.3.1. The source corpus contains 870,514 applications with 48 metadata features. Of these, 803,320 applications (92.3%) were matched to VirusTotal reports, 772,706 (88.7%) were successfully parsed from Quark-Engine scans, and 772,506 (88.7%) had paired analyses from both sources (with an additional 27 applications having VirusTotal data only, included in the summary dataset). The released paired dataset combines the original metadata with ten selected derived features—five from VirusTotal and four from Quark-Engine—yielding 58 analytical features per application, or 59 columns when the MD5 linkage key is included. The release contains processed extensive tables, a compact summary dataset, and five consensus-based validated subsets whose sizes range from 38,302 to 80,368 applications depending on the VirusTotal threshold.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"68 ","pages":"Article 113152"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148778112","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":"PUST Cafeteria Food Image Dataset: Real-world Bangladeshi meal-platter images with bounding-box and polygon annotations","authors":"Marufa Yeasmin,&nbsp;Md. Razu Ahmed,&nbsp;Protiva Rani Kundu,&nbsp;Jannatul Mauya,&nbsp;Md. Shamim Reza","doi":"10.1016/j.dib.2026.113262","DOIUrl":"10.1016/j.dib.2026.113262","url":null,"abstract":"<div><div>The PUST Cafeteria Food Image Dataset is a real-world image resource developed for object detection and instance segmentation of mixed Bangladeshi cafeteria meals. The source collection comprises 720 meal-platter photographs acquired between 1 August and 5 September 2025 at the Central Cafeteria of Pabna University of Science and Technology, Bangladesh. Images were captured using a Realme GT Master Edition and an OPPO A92 during routine cafeteria operations without standardized illumination, camera position, camera-to-platter distance, background, plate configuration, or food arrangement. Each identifiable food instance was manually annotated in Roboflow using a bounding box and polygon vertices delineating its visible region. Following auto-orientation, resizing, selective augmentation, light class balancing, and quality review, 1,728 images containing 6,297 annotated food instances across 16 classes were retained in the final processed dataset. The dataset was organized into training, validation, and test subsets comprising 1,512, 108, and 108 images, respectively. Annotations are provided in YOLO-compatible TXT and COCO JSON formats, supporting reuse across object-detection and instance-segmentation frameworks. The dataset can be reused for regional food-image analysis, comparative benchmarking, transfer learning, domain adaptation, and intelligent food-service research.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113262"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149046329","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 on proximate composition, starch content, agronomic performance, and GGE biplot analysis of newly developed maize hybrids across three cropping systems in Indonesia","authors":"Frengky Arya Saputra,&nbsp;Adimas Pamuji,&nbsp;Ade Ismail,&nbsp;M. Rafi Fauzan,&nbsp;Dedi Ruswandi","doi":"10.1016/j.dib.2026.113267","DOIUrl":"10.1016/j.dib.2026.113267","url":null,"abstract":"<div><div>This data article presents a dataset on proximate composition, starch content, and agronomic performance of newly developed maize hybrids evaluated across three cropping systems in Indonesia: sole maize cropping, maize–soybean intercropping, and maize–sweet potato intercropping. Because all three cropping systems were established at a single location and growing season, they are treated here as management treatments rather than independent environments, and genotype × cropping-system interaction is analyzed accordingly. Eighteen newly developed maize hybrids and one commercial check hybrid were tested using a randomized complete block design with three replications. The dataset includes seven grain quality traits, namely moisture, ash, fat, protein, carbohydrate (calculated by difference from the other proximate traits), crude fibre, and starch content, as well as four agronomic traits (number of leaves, leaf length, leaf width, and chlorophyll content) and grain yield. Combined analysis of variance showed significant effects of cropping system, genotype, and genotype × cropping-system interaction for most traits, indicating substantial variation among hybrids and cropping systems. Genotype × cropping-system mean values for all traits, heatmap visualizations, and GGE biplot analyses (discriminativeness/representativeness, mean performance/stability, and which-won-where patterns) are provided, and the complete dataset is available in the Mendeley Data repository. This dataset is useful for maize breeders, food and feed researchers, and crop scientists interested in selecting maize hybrids with superior nutritional quality, starch potential, yield performance, and adaptation to diversified tropical cropping systems.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"69 ","pages":"Article 113267"},"PeriodicalIF":1.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149046334","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}