Data in Brief最新文献

{"title":"RNA-seq dataset of the estrogen-dependent regulation of the transcriptome in mouse mammary gland organoids","authors":"Aurélie Lacouture ,&nbsp;Mame Sokhna Sylla ,&nbsp;Lucas Germain ,&nbsp;Étienne Audet-Walsh","doi":"10.1016/j.dib.2025.111984","DOIUrl":"10.1016/j.dib.2025.111984","url":null,"abstract":"<div><div>The mammary gland development <em>in utero</em> and during life is strongly regulated by hormones. To study the genes regulated specifically by the estrogen signalling pathway in the epithelial compartment, we treated mouse mammary epithelial organoids with estradiol, the most potent endogenous estrogen. At maturity, after 11 days of treatment, organoids were collected, and RNA was purified for next-generation sequencing. The bulk mRNA-seq data obtained were verified for raw quality, and reads were pseudo-aligned on the murine reference transcriptome (Gencode vM25). Differentially expressed genes were identified using DESeq2 to gain a better understanding of the impact of estrogens on the mammary epithelial cell transcriptome <em>ex vivo</em>. These data can be reanalyzed and combined with recent single-cell RNA-seq data to study the estrogen-dependent transcriptome at the cellular level and better understand the functional impact on the mammary gland in physiopathological conditions, such as during lactation, following endocrine-disrupting chemical exposure, or through the course of carcinogenesis.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111984"},"PeriodicalIF":1.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890701","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 comprehensive dataset of rice leaf images for disease detection using machine learning","authors":"Afif Hasan ,&nbsp;Tanvir Almas Layes ,&nbsp;Arafat Sahin Afridi ,&nbsp;Shakhawath Hossain Rifat ,&nbsp;Fernaz Narin Nur ,&nbsp;Nazmun Nessa Moon","doi":"10.1016/j.dib.2025.111977","DOIUrl":"10.1016/j.dib.2025.111977","url":null,"abstract":"<div><div>This manuscript presents a comprehensive, expert-annotated dataset comprising 19,000 rice leaf images, including 2,753 original images and 16,247 augmented images, sourced from the Bangladesh Rice Research Institute (BRRI). The dataset includes seven disease classes: Healthy (603 original images), Rice Blast (696 original images), Scald (421 original images), Leaf-folder Injury (247 original images), Insect Infestation (281 original images), Rice Stripes (266 original images), and Tungro Disease (239 original images). These images, captured under varying environmental conditions using smartphone cameras, accurately reflect real-world conditions. The images have been meticulously annotated by agronomy experts for reliable disease labeling. To enhance dataset diversity, data augmentation methods such as rotation, scaling, brightness adjustment, and horizontal flipping were systematically applied, expanding the dataset by creating additional variants from the original images. The dataset serves as a rich resource for developing machine learning models for the automatic detection of rice diseases. This initiative aims to enable early disease detection, promote sustainable farming practices, and improve food security, particularly in rice-dependent developing countries.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111977"},"PeriodicalIF":1.4,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887372","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":"CT-SCOPE: annotated dataset of CT SCans for the automatic semantic segmentation of the Osseous structures of the Paranasal sinusEs","authors":"Yichun Sun,&nbsp;Alejandro Guerrero-López,&nbsp;Julián D. Arias-Londoño,&nbsp;Juan I. Godino-Llorente","doi":"10.1016/j.dib.2025.111962","DOIUrl":"10.1016/j.dib.2025.111962","url":null,"abstract":"<div><div>This article introduces a dataset of computed tomography (CT) scans of the paranasal sinuses collected from 6 distinct hospitals, using 4 different CT devices, and ensuring diverse recording conditions. The dataset includes CT axial scans from 40 subjects, 13 of which were manually annotated with a semantic segmentation of the osseous structures of the area surrounding the paranasal sinuses, while the remaining 27 subjects contain unannotated CT scans. The data was organized into raw DICOM files and was also stored as uncompressed PNG images. The dataset includes an average of 212±105 slices per subject, while the annotated subset contains 696 masks paired with their corresponding CT slice. To further enhance the dataset, a set of automatically delineated masks (i.e., pseudo-labels) is also included for the unannotated CT scans. This dataset is highly valuable for medical image analysis, particularly to train and evaluate deep learning sematic segmentation models to identify the osseous structures surrounding the paranasal sinuses, as well as to explore domain adaptation techniques across different imaging devices. Additionally, it supports research in areas such as resolution enhancement and cross-device generalization, positioning it as an essential resource for advancing the robustness and generalizability of artificial intelligence driven medical image analysis tools.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111962"},"PeriodicalIF":1.4,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903056","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":"Stress-strain and fracture acoustic emission dataset of high-strength concrete for metro tunnel lining under different stress states","authors":"Hongliang Tu ,&nbsp;Zhiming Han ,&nbsp;Zhiwei Wang ,&nbsp;Peipei Chen ,&nbsp;Guangchang Yang","doi":"10.1016/j.dib.2025.111973","DOIUrl":"10.1016/j.dib.2025.111973","url":null,"abstract":"<div><div>The maintenance of metro tunnel support structures is crucial for ensuring the safe and efficient operation of urban rail transit. Under complex stress conditions (including tension, compression, shear, torsion), metro tunnel linings are susceptible to various forms of damage, such as cracking, spalling, segment misalignment, and water leakage. These issues pose substantial challenges to tunnel safety and service life. To address these engineering problems and facilitate related scientific research, this paper presents a novel dataset capturing the stress-strain behavior and fracture acoustic emission (AE) characteristics of high-strength concrete used in metro tunnel linings under different stress states. The dataset was acquired using advanced equipment, including the MTS815.04 (Mechanics Testing System), AE sensors, stress-strain sensors, and high-resolution cameras. It provides valuable resources for scientific research on metro tunnel construction and concrete materials. The dataset includes images of lining cracking, stress-strain data of lining concrete, images of concrete damage, and acoustic emission data during the damage process. It serves as a valuable asset for multiple research domains, including safety assessment of metro tunnel linings, concrete material properties and constitutive models, and deep learning study. Detailed annotations for each image and each set of experimental data are also provided to ensure accurate basic data support for relevant scientific research. Additionally, the data collection process is discussed, the importance of metro tunnel construction is emphasized, and the dataset’s potential applications in analyzing tunnel lining safety and researching concrete material performance are highlighted. The research community is encouraged to utilize this dataset to advance the cutting-edge studies related to metro tunnel construction and new material development.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111973"},"PeriodicalIF":1.4,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865298","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 quantifying the beach litter from Manado Bay (northern Sulawesi, Indonesia), which lies in the Coral Triangle, over a 5-year period (2018-2022)","authors":"Veibe Warouw ,&nbsp;Jane M. Mamuaja ,&nbsp;Markus T. Lasut","doi":"10.1016/j.dib.2025.111969","DOIUrl":"10.1016/j.dib.2025.111969","url":null,"abstract":"<div><div>Data is presented on the macro and meso size, weight, and number of items for a variety of beach litter types collected from Manado Bay, Northern Sulawesi, Indonesia, which lies within the Coral Triangle. The data, both raw and partly processed, were collected over 5 years (2018 to 2022) using the internationally standard method for monitoring marine debris, which has been adopted by Indonesia. The classification is based on 9 material types: (1) plastics (PL), (2) foamed plastics (FP), (3) cloth (CL), (4) glass and ceramics (GC), (5) metal (ME), (6) other type of litter (OT), (7) paper and cardboard (PC), (8) rubber (RB), and (9) wood (WD), and further broken down into subcategories. These data show an increasing trend of waste, with plastic bottles (&lt;2 L) reaching peak weights of 1,125.46 g (2020) and 807.68 g (2022), and clothing waste soaring to 6,088.80 g (2022). These data are compatible with data collected on marine litter pollution in other parts of Coral Triangle, as well as the rest of the world. We anticipate that these will be of value for researchers and other stakeholders, providing information on marine litter for monitoring and decision making purposes.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111969"},"PeriodicalIF":1.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865294","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 spatially distributed rainfall dataset for West Java, Indonesia","authors":"Dwi Yoga Primartono ,&nbsp;Rahmat Hidayat ,&nbsp;Supari ,&nbsp;Rakhmat Prasetia ,&nbsp;Muh Taufik","doi":"10.1016/j.dib.2025.111974","DOIUrl":"10.1016/j.dib.2025.111974","url":null,"abstract":"<div><div>Rainfall data availability is a basis of climate analysis and application, but its spatial distribution based on observed rainfall at local scale remains a research challenge. A spatially distributed rainfall at a finer resolution is the foundation for coping uncertain climate change and water resource planning and management. Here, we established a daily grid dataset for observed rainfall of West Java, Indonesia. The data were <em>from</em> 1991-2020 at daily resolution from 162 rain gauges covering various terrains and climate zone, which were monitored by <em>the Indonesian Agency for Meteorology Climatology and Geophysics</em> (BMKG). We used the inverse distance weighting (IDW) approach to spatially interpolate rainfall at <em>0.05⁰</em> grid resolution. In addition, timeseries of monthly and annual rainfall were generated from the daily dataset. Further, the spatial rainfall data <em>is</em> useful for identifying local climate, adaptation strategy for hydro-meteorological hazard, and water resource planning.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111974"},"PeriodicalIF":1.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865295","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 histopathology image dataset for Johne’s disease detection and research","authors":"Wael Hananeh ,&nbsp;Mohammad Fraiwan","doi":"10.1016/j.dib.2025.111975","DOIUrl":"10.1016/j.dib.2025.111975","url":null,"abstract":"<div><div>Johne’s disease (paratuberculosis), caused by <em>Mycobacterium avium</em> subspecies <em>paratuberculosis</em>, is a chronic intestinal infection that affects ruminants and poses significant challenges for livestock health and management. Accurate and early diagnosis is of paramount importance for effective disease control, yet traditional histopathological assessment requires expert interpretation and remains subject to interobserver variability. In this paper, we present a curated dataset of histopathological slide images collected from tissue samples confirmed to be positive or negative for Johne’s disease. The samples were processed and stained using standard hematoxylin and eosin (H&amp;E) protocols, and the slides were digitized using a high-resolution microscope camera. Each image is annotated with a diagnostic label verified by a board-certified pathologist. The dataset is organized by disease status (i.e., positive vs. negative), which makes it useful for supervised machine learning applications, computer-aided diagnosis, and digital pathology research. In addition to supporting the development of automated detection systems, the dataset serves as a valuable educational resource for training veterinary pathology students in recognizing histological patterns associated with MAP infection. To the best of our knowledge, this is the first publicly available dataset of histopathology images dedicated to Johne’s disease.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111975"},"PeriodicalIF":1.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865299","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":"RoseLeafInsight: A high-resolution image dataset for rose leaf disease recognition","authors":"Arnob Das Shacha,&nbsp;Sabbir Hossain Durjoy,&nbsp;Md. Emon Shikder,&nbsp;Md Mostafa Kamal,&nbsp;Md Mehedi Hasan Shoib,&nbsp;Md Hasan Imam Bijoy","doi":"10.1016/j.dib.2025.111968","DOIUrl":"10.1016/j.dib.2025.111968","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The Rose (genus Rosa) has become a significant factor in the Bangladeshi flower industry, both in terms of exports and local consumption. However, rose farming in this country faces serious challenges due to diseases affecting its leaves, which weaken the plants and result in lower flower yields and financial losses for farmers. Rosa (genus Rosa) is one of the most attractive and commercially valuable flower genera. However, agricultural rose production faces several challenges, such as pesticide resistance, which affects plant growth and results in a reduced quantity and quality of healthy flowers. Several natural factors also cause interference with rose production. Most farmers involved in this industry have limited education, which hinders their ability to identify early-stage rose-leaf disease solely through visual inspection. Furthermore, limited communication with agricultural experts exacerbates the situation, leading to delayed interventions and economic losses. This study presents the rose leaf disease dataset, which would help enhance disease tracking, diagnosis, and research in roses. From October 2024 to January 2025, large-scale field surveys were conducted to capture quality images for each condition class in rose leaves. In this paper, four classes comprise ‘Black Spot,’ ‘Insect Hole,’ ‘Yellow Mosaic Virus,’ and ‘Healthy,’ representing different stages in disease progression. There are 3,228 original images, categorized as follows: Black Spot (409), Insect Hole (453), Yellow Mosaic Virus (680), and Healthy (1,686). During the pre-processing stage, the images are resized to 3000×3000 pixels, and low-quality, duplicate, or irrelevant images are removed to ensure high quality. We have employed various augmentation techniques, including rotation, flipping, contrast adjustment, blurring, shearing, zooming, and noise addition, to increase the dataset size and enhance model generalization. Datasets like this one are in high demand for agricultural research, leading to improved disease management and increased yields. These goals can be achieved through high-accuracy machine-learning models for early disease detection and cause identification. This gives the farmers more time to take necessary actions for disease prevention and pest control. This tech-based system combines the field of agriculture with the cutting edge of computer science and AI, making precision agriculture even more effective and efficient. Our dataset is designed to meet the need for data to train these models and provide a baseline benchmark for disease detection in our specific crop, the Rose. Improvements in different generations of models, as well as numerous other forms of scientific advancements, can lead to further increases in efficiency and ultimately result in better, smarter farms. In our initial testing for categorizing rose leaves, we employed two well-known transfer learning models. Among them, MobileNetV2 performed exceptionally well, achieving","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111968"},"PeriodicalIF":1.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865288","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":"Recognizing and understanding stress in adults during Covid-19: Data insights from the corona health app","authors":"Michael Winter ,&nbsp;Thomas Probst ,&nbsp;Dennis John ,&nbsp;Rüdiger Pryss","doi":"10.1016/j.dib.2025.111967","DOIUrl":"10.1016/j.dib.2025.111967","url":null,"abstract":"<div><div>The dataset presented in this work is derived from the Stress Recognition Study in the Corona Health app, a digital health platform designed with the German Robert Koch Institute (RKI) to monitor stress levels and associated factors in adults during and after the COVID-19 pandemic. Data were collected using a mobile-based survey completed by 627 adults (18 years and older) at baseline, with 385 of these participants also contributing 4,331 follow-up assessments over time. The study utilized baseline and follow-up questionnaires to capture changes in participants' stress levels throughout the pandemic period and beyond (December 2020 to May 2025). The questionnaires cover key stress indicators such as perceived stress levels, demographic factors, and smartphone sensor data. By capturing real-time, longitudinal stress data from adults during a public health crisis, this dataset enables researchers to examine how stress levels fluctuated in response to pandemic restrictions and recovery phases. The integration of ecological momentary assessments with mobile sensing data (e.g., app usage statistics, coarse-grained location information) provides opportunities to analyze adult stress trajectories, identify stress resilience factors, and evaluate the effectiveness of mobile health approaches for stress monitoring during crisis situations. The data, including questionnaire responses and mobile sensing data, are publicly available under a Creative Commons license at <span><span>https://zenodo.org/records/15780255</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111967"},"PeriodicalIF":1.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865296","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":"Quantitative proteomic data of flotillin-2 interactome within detergent resistant membranes of HeLa cells","authors":"Anita Hryniewicz-Jankowska ,&nbsp;Michał Tracz ,&nbsp;Ilona Opiełka ,&nbsp;Katarzyna Augoff ,&nbsp;Aleksander Czogalla ,&nbsp;Aleksander F. Sikorski","doi":"10.1016/j.dib.2025.111970","DOIUrl":"10.1016/j.dib.2025.111970","url":null,"abstract":"<div><div>Flotillin-binding protein networks serve as scaffolds, organizing lipid rafts and facilitating the recruitment of other raft-associated proteins such as receptors and downstream signaling molecules to regulate various intracellular pathways, including those involved in cell proliferation, migration, and endocytosis.</div><div>Flotillins belong to the SPFH (stomatin/prohibitin/flotillin/HflK/C) domain-containing protein family, also known as the prohibitin homology (PHB) domain, which enables membrane association via acylation and hydrophobic hairpin motifs that anchor them to the inner leaflet of the plasma membrane. The functional diversity of flotillin proteins within membrane microdomains primarily stems from their interactions with other proteins.</div><div>Data presented in this article characterize the proximal interactome of flotillin-2 within detergent-resistant membranes (DRMs) using BioID, a proximity-dependent biotinylation technique. Flotillin-2 was fused with the biotin ligase BirA* at either the N- or C-terminus and expressed in HeLa cells. DRMs were isolated through sucrose density gradient ultracentrifugation, and biotinylated proteins were purified using biotin–avidin affinity followed by label-free quantitative (LFQ) mass spectrometry.</div><div>This approach identified a set of proteins significantly enriched in DRM fractions from cells expressing flotillin-2–BirA* fusions compared to control cells expressing BirA*–mEGFP. The two analyses allowed for relative quantification of ∼433 and ∼926 unique proteins across the N-terminal and C-terminal BirA* fusions, respectively. 28 (N-terminal) and 88 (C-terminal) proteins were observed as significantly enriched in flotillin-2 samples (28 and 43 with fold change ≥ 2). These enriched proteins are candidate interactors of flotillin-2 within membrane raft domains. Notably, the N-terminal fusion-associated proteins were significantly linked to specific biological processes such as dendritic transport and regulation of signal transduction, whereas the C-terminal fusion group showed enrichment in membrane biogenesis-related proteins. Among here presented DRM partners of flotillin-2, both previously known and earlier unreported interactors of this protein were found. Overall, our BioID-based analysis provides valuable insight into the flotillin-2 interactome in DRM fractions and lays the groundwork for future studies exploring the regulation of membrane lateral heterogeneity and the role of flotillin-mediated domains in signaling pathways, particularly those dysregulated in diseases such as cancer.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 111970"},"PeriodicalIF":1.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887374","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}