Data in Brief最新文献

{"title":"A transcriptome sequence dataset characterizing eggs, nymphs and adults of Oxycarenus hyalinipennis, the cotton seed bug","authors":"Sam D. Heraghty,&nbsp;Aijun Zhang,&nbsp;Daniel Kuhar,&nbsp;Dawn E. Gundersen-Rindal,&nbsp;Michael E. Sparks","doi":"10.1016/j.dib.2026.112532","DOIUrl":"10.1016/j.dib.2026.112532","url":null,"abstract":"<div><div>The cotton seed bug, <em>Oxycarenus hyalinipennis,</em> is an agricultural pest that has recently been detected in the United States and has the potential to cause extensive economic damage to the cotton production industry. Currently, there are no transcriptomic resources for this species. The data reported here will serve to help guide future efforts to create additional reference resources as well as facilitate the development of population control strategies. These data could also be of use towards identifying protein coding genes in a cotton seed bug genome assembly. A total of 13,384 differentially expressed genes was identified, which collectively encoded 40,871 distinct transcripts, of which 18,842 could be annotated with a reference protein in the NCBI NR database, 13,233 with Pfam protein families and 8,089 with GO Gene Ontology terms. These transcripts could, for example, be targeted for future functional genomics work.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112532"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole genome sequencing data analysis identified a cefotaxime-resistant Empedobacter brevis GBW-1 isolate from ground beef encoding a novel metallo-beta-lactamase variant, blaEBR-6","authors":"Daniel Jones ,&nbsp;Praful Aggarwal ,&nbsp;Jamison Trewyn ,&nbsp;Poojhaa Shanmugam ,&nbsp;Kyle Leistikow ,&nbsp;Troy Skwor","doi":"10.1016/j.dib.2026.112547","DOIUrl":"10.1016/j.dib.2026.112547","url":null,"abstract":"<div><div>While investigating foodstuffs for ESBL-producing <em>Aeromonas</em> species on ampicillin dextrin agar with vancomycin and cefotaxime, a multidrug-resistant <em>Empedobacter brevis</em> strain GBW-1 was identified from ground beef. Phylogenetic analysis supports the interconnectedness of environment, humans and food driving this species' evolutionary development. Antimicrobial susceptibility testing demonstrated resistance to gentamicin, carbapenems and third-generation cephalosporins. Data collection from whole genome sequencing of this strain detected a 3.74 Mb genome with 32.8% GC content containing 3780 coding genes. Among these genes, at least three known antimicrobial resistance (AMR) genes were identified from the dataset with <em>qacG, vanT</em> gene within the <em>vanG</em> cluster, and a novel variant of the metallo-β-lactamase <em>bla</em>_EBR-6. This homologue, EBR-6, was compared against previously known EBR variants and was found to be closest to EBR-3 with an 84.98% amino acid identity match. Data collection from <em>in silico</em> molecular docking experiments predicted these mutations change the binding to meropenem. Furthermore, nearly 100 annotated regions associated with mobile genetic elements, including the presence of <em>tra</em> operons, were identified on the genome. Together, this dataset provides, genomic, phenotypic, and <em>in</em> silico data that may be reused to monitor the evolution of EBR from a One Health perspective.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112547"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset on resource allocation and usage for a private cloud","authors":"Paola Marques,&nbsp;Mariana Mendes,&nbsp;Thiago Emmanuel Pereira,&nbsp;Giovanni Farias","doi":"10.1016/j.dib.2026.112514","DOIUrl":"10.1016/j.dib.2026.112514","url":null,"abstract":"<div><div>While public cloud providers dominate the commercial landscape, private clouds are widely adopted by academic and research institutions to meet specific governance and operational requirements. There are multiple available datasets about resource usage of public clouds; however, datasets capturing usage patterns in private clouds remain scarce, which limits research in this area. This work presents a dataset comprising over 64 million records collected from a private OpenStack-based cloud operated by the Distributed Systems Laboratory at the Federal University of Campina Grande, Brazil. Data was continuously gathered over nearly twelve months (May 23, 2024 to May 16, 2025), periodically querying OpenStack APIs and monitoring services every five minutes. The dataset captures different aspects of the infrastructure, allocation quotas, user-to-project associations (as OpenStack groups users into projects), server (virtual machines) specifications, and resource utilization for users and projects. Entries are timestamped, enabling temporal analyses of system dynamics. Sensitive attributes, such as user names, project names, IP addresses, and server names were protected, leaving only system-generated UUIDs. By offering a detailed, time-stamped, view of a private cloud, this dataset provides a valuable resource for cloud computing research, helping to bridge the gap in publicly available datasets from non-commercial cloud environments. The dataset is valuable not only for academic institutions but also for companies considering cloud repatriation.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112514"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agri-vision Bangladesh: A multi-crop augmented image dataset for automated disease diagnosis in Bottle Gourd, Zucchini, Papaya, and Tomato","authors":"Md Masum Billah ,&nbsp;Md. Anisur Rahman ,&nbsp;Saifuddin Sagor ,&nbsp;Sanzida Parvin ,&nbsp;Mohammad Shorif Uddin","doi":"10.1016/j.dib.2026.112528","DOIUrl":"10.1016/j.dib.2026.112528","url":null,"abstract":"<div><div>This article introduces Agri-Vision Bangladesh, a comprehensive, augmented image dataset designed to advance automated disease diagnosis in four economically vital agricultural crops: Bottle Gourd (<em>Lagenaria siceraria</em>), Zucchini (<em>Cucurbita pepo</em>), Papaya (Carica papaya), and Tomato (<em>Solanum lycopersicum</em>). Addressing the scarcity of region-specific agricultural data, a total of 5266 original images were acquired directly from diverse agricultural fields in Bangladesh using a SONY ALPHA 7 II full-frame camera under natural lighting conditions. The dataset encompasses 28 distinct classes, covering a wide spectrum of biotic stressors including viral (Mosaic Virus, Leaf Curl), fungal (Downy Mildew, Anthracnose, Alternaria Blight), bacterial (Bacterial Blight, Xanthomonas), and pest-induced damage (Insect Hole, White Spot), alongside Healthy samples. To ensure scientific reliability, each image underwent a rigorous two-stage validation process by senior agronomists. To tackle class imbalance and facilitate the training of data-intensive Deep Learning models, the dataset was expanded using a Python-based augmentation pipeline incorporating geometric transformations (rotation, flipping) and photometric adjustments (noise, brightness) resulting in a final repository of 28,000 images (5266 original and 22,734 augmented). All files are standardized to 512×512 pixels in JPG format. This expert-validated resource serves as a critical benchmark for developing robust computer vision algorithms (e.g., CNNs, Vision Transformers) for precision agriculture, enabling research into fine-grained classification, object detection, and cross-crop transfer learning in subtropical farming environments.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112528"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualizing archaeobotanical data: A comprehensive photographic record of desiccated plant remains from an early modern context at Santi Quattro Coronati, Rome","authors":"Claudia Moricca ,&nbsp;Rachele Nicolini ,&nbsp;Lucrezia Masci ,&nbsp;Lia Barelli ,&nbsp;Simona Morretta ,&nbsp;Raffaele Pugliese ,&nbsp;Laura Sadori","doi":"10.1016/j.dib.2026.112468","DOIUrl":"10.1016/j.dib.2026.112468","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The “Santi Quattro Coronati – archaeobotanical plates” dataset presents a comprehensive photographic collection of carpological remains recovered from a pit in the complex of Santi Quattro Coronati (Rome, Italy). The deposit, dated between the late 15th and the mid-16th century, yielded a diverse assemblage of desiccated plant remains. The dataset is novel in that it provides the complete photographic documentation of all identified taxa from a single Early Modern archaeological context, a chronological phase that remains underrepresented in Italian archaeobotanical research.&lt;/div&gt;&lt;div&gt;The photographic documentation focuses on a representative sample of each taxon identified in the archaeobotanical analysis, with particular attention to the best-preserved specimens. When multiple plant parts of the same taxon were present, all were included. The dataset also includes fragile and rarely illustrated plant parts, such as cereal rachis fragments, tunics and basal plates of onion and garlic, grapevine tendrils and legume seed coats. These are often excluded from reference atlases due to their low archaeological survivability and the consequent scarcity of well-preserved comparative specimens.&lt;/div&gt;&lt;div&gt;High-resolution images were acquired using a Leica MC205C stereomicroscope equipped with a Leica IC80HD camera and the Leica Application Suite v.4.5.0 software. Illumination was provided by the Leica LED5000 HDI™ dome system, ensuring constant, diffuse light conditions. A column of images was captured for each specimen and processed with Helicon Focus v.7.0.1 Pro through focus stacking to obtain a single fully focused image. Depending on specimen size and complexity, between 9 and 127 photographs were used per perspective. Larger samples, unsuitable for microscopic observation, were photographed using a Canon digital camera under controlled illumination. Post-processing was performed with GIMP, applying standard tools for background cleaning and masking. Each final plate includes a scale bar for size reference.&lt;/div&gt;&lt;div&gt;The dataset is organized alphabetically by plant family and taxon. For each taxon, one or more plates are provided, displaying specimens from one to three perspectives to represent their 3D morphology. Nomenclature follows the taxonomy used in the original publication of the assemblage and has been updated according to the most recent checklist of the Italian vascular flora. A metadata .xls file is provided to facilitate consultation, reuse, comparison and integration with other archaeobotanical datasets.&lt;/div&gt;&lt;div&gt;This dataset offers a well-documented comparative visual reference for species/genus identification and for assessing the preservation state and morphological integrity of desiccated archaeobotanical remains. Offering detailed photographic records of New World plant taxa previously identified in this context, the study enhances accessibility and understanding of these materials through visual reference. Despite bein","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112468"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reference-grade genome assembly data of sika deer in Hokkaido, Japan, Cervus nippon yesoensis","authors":"Yuki Matsumoto ,&nbsp;Junco Nagata ,&nbsp;Yukiko Matsuura ,&nbsp;Hayato Iijima","doi":"10.1016/j.dib.2025.112423","DOIUrl":"10.1016/j.dib.2025.112423","url":null,"abstract":"<div><div>Sika deer (<em>Cervus nippon</em>) is naturally distributed across East Asia and includes 14 subspecies, showing phenotypic and genetic diversity. In this study, we constructed a de novo genome assembly of wild sika deer using one of the largest subspecies, <em>C. n. yesoensis</em>. We used HiFi, high quality long-read based on Pacific Bioscience to assemble our novel genome assembly CerNipYes1.0. The genome size of CerNipYes1.0 is estimated to be 3.1Gb, which is 0.6Gb larger than the other genome assembly of sika deer previously reported. The number of scaffolds is 1,810 and N50 length achieved 77Mb. Compleasm, a genome completeness evaluation tool based on Benchmarking Universal Single-Copy Orthologs (BUSCO) indicated that 12,562 (99.75%) genes are completed as genes with comparing to database. Our results indicate that CerNipYes1.0 is valuable to study the molecular biology, phylogeny and evolution of the Cervidae and its genome.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112423"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BrinjalFruitX: A field-collected image dataset for machine learning and deep learning-based disease identification in brinjal fruits","authors":"Abu Kowshir Bitto ,&nbsp;Md. Zahid Hasan ,&nbsp;Md. Hasan Imam Bijoy ,&nbsp;Khalid Been Badruzzaman Biplob ,&nbsp;Mohammad Mahadi Hassan ,&nbsp;Mohammad Shohel Rana ,&nbsp;Abdul Kadar Muhammad Masum","doi":"10.1016/j.dib.2026.112490","DOIUrl":"10.1016/j.dib.2026.112490","url":null,"abstract":"<div><div>Brinjal (Solanum melongena) or eggplant is one of the four most essential vegetable crops that are grown in Bangladesh and contribute significantly to the agricultural industry of the country. Brinjal supports the livelihood of numerous small farmers; however, brinjal is severely susceptible to various fruit diseases, which have serious impacts on yield quality and may cause considerable economic losses. While most existing plant disease datasets primarily focus on leaf-related disorders, only a limited number include fruit-related diseases and even those contain very few classes. This gap is significant because fruit diseases directly affect crop quality, market value, and overall yield. This is why we present here a new and comprehensive dataset that is unparalleled, exclusively for brinjal fruit diseases. This data set consists of 1823 high-quality, labelled images, across five distinct classes: Phomopsis Blight, Shoot and Fruit Borer, Fruit Cracking, Wet Rot, and Healthy Fruit. The images were collected from real farm conditions in numerous areas of Bangladesh to ensure a robust sample of varied environmental and farming practices impacting the growth of diseases. This dataset is designed with the unique aim to support plant disease research and enhance training of deep learning models for autonomous disease detection. Lastly, the dataset will allow early disease detection, enhancing crop management practice, reduction of losses, and increasing farmers' economic returns. The release of this dataset will encourage agricultural research as well as practical use in precision agriculture.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112490"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fully synthetic textual dataset of student learning habits and preferences generated using a large language model","authors":"Mehedi Hasan","doi":"10.1016/j.dib.2026.112512","DOIUrl":"10.1016/j.dib.2026.112512","url":null,"abstract":"<div><div>Educational data mining and learning analytics have become important research areas for supporting pedagogical analysis, algorithm development, and privacy-preserving educational research. The advancement of natural language processing (NLP) methods in educational contexts depends on the availability of structured and well-documented textual datasets; however, access to real student data is often restricted due to ethical, legal, and privacy concerns. This article presents a fully synthetic textual dataset of student learning habits and preferences generated using a large language model (LLM). The dataset contains 10,000 CSV-formatted records representing fictional students and includes attributes such as education level, study hours, preferred learning methods, learning challenges, motivation levels, opinions on online learning, and primary devices used for study. Data generation was performed using structured prompting strategies with explicitly defined controlled vocabularies to ensure internal consistency and reproducibility while avoiding the use of any real personal information. The resulting dataset follows intentionally controlled and near-uniform distributions, with variables generated under independent constraints. This design limits its suitability for modelling real-world stochastic behaviour or discovering natural correlations but makes it appropriate for benchmarking educational NLP pipelines, evaluating synthetic data generation techniques, and conducting privacy-preserving survey and machine learning experiments.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112512"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Species climate index data for United Kingdom invertebrates","authors":"Robin J. Pakeman","doi":"10.1016/j.dib.2026.112500","DOIUrl":"10.1016/j.dib.2026.112500","url":null,"abstract":"<div><div>Numerous approaches have been used to assess the response of species to changing climate. One of the simplest is the calculation of indices which describe the climate of areas occupied by different species and uses them to assess community level change or to assess if species’ trends are predictable from the climate of their ranges. The paper describes the calculation of Species Climate Indices for 4924 UK invertebrate species from freshwater and terrestrial ecosystem by combining information from occurrence records and historical climate data. The indices calculated are the mean January temperature, mean July temperature and mean annual precipitation of 10 km x 10 km squares occupied by the species during the period used for calculating the climate data (1991–2020). These data have been used to assess if trends in occupancy are correlated to species’ climate indices [<span><span>1</span></span>] but are also ideally used for looking at trends within communities if repeat sampling has been carried out.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112500"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dataset of 12,161 steel rebar tests from sudanese construction projects (2016-2022)","authors":"Amged O. Abdelatif,&nbsp;Abdelrahim H. Abdelrahim,&nbsp;Gamar-Aldwla S. Shangray,&nbsp;Mohammed-Alfatih Mustafa,&nbsp;Mustafa M. Abaker,&nbsp;Yahia A. Idris,&nbsp;Abdelrahim M. Yousif","doi":"10.1016/j.dib.2026.112469","DOIUrl":"10.1016/j.dib.2026.112469","url":null,"abstract":"<div><div>This data article describes a comprehensive dataset comprising 12,161 individual steel reinforcement bar tensile tests (3,898 test reports) collected from various construction projects across Sudan between 2016 and 2022. The data was systematically extracted from official test reports generated by the University of Khartoum, Faculty of Engineering, Department of Civil Engineering, Material and Structures Testing Laboratory. The purpose of this dataset is to establish a verified, large-scale baseline of material performance for Sudanese reinforcement steel, providing transparent and verifiable raw values of key mechanical and dimensional properties for locally sourced rebars with tested diameters ranging from 8 mm to 32 mm. This data is intended for reuse to conduct rigorous analyses on steel reinforcement quality and characteristic properties in Sudan, offering a unique baseline for regional construction quality and providing a representative performance benchmark applicable to other developing countries.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"65 ","pages":"Article 112469"},"PeriodicalIF":1.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}