Data in Brief最新文献

{"title":"Smart home environment data across 4 European countries","authors":"Stefan Winterberger,&nbsp;Dmitriy An,&nbsp;Martin Biallas,&nbsp;Andrew Paice","doi":"10.1016/j.dib.2025.111636","DOIUrl":"10.1016/j.dib.2025.111636","url":null,"abstract":"<div><div>This paper describes a dataset of anonymised smart home environment data that was collected during a project over 359 days (16.05.2023-08.05.2024). The dataset contains information about temperature (°C), humidity (%), ambient light (lux), CO₂ (ppm), VOC (ppm), sound pressure level (dB) in a time interval of 2–5 min in addition to event based data from PIR-Sensors and door contact sensors. Additionally, time and location information for each data point is available in the form of a time stamp, the user ID, the room and the country. The dataset was collected in 4 different European countries from a total of 62 users at their residential settings. Different installations had different sets of sensors, meaning not all parameters were measured at every location. The target group for the field trials was elderly people 65+.</div><div>During the project it could be shown that the data can be used to estimate presence in a room, based on the environmental data only, where the output of the PIR-Sensors were used as proxy labels. The weakness of the dataset is the lack of validated ground truth, which makes supervised learning approaches difficult. The strength of the dataset lies in the variety of sensors including sound pressure and the long period (nearly 1 year) of high frequency measurements in different countries.</div><div>Collecting data in real-world residential settings is challenging, but by making this dataset publicly available, we provide researchers with a valuable resource to explore smart home applications, presence detection, and environmental monitoring in everyday life.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111636"},"PeriodicalIF":1.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070219","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":"Time-lapse 3D image datasets of spruce tree wood enzymatic deconstruction","authors":"Solmaz Hossein Khani ,&nbsp;Noah Remy ,&nbsp;Khadidja Ould Amer ,&nbsp;Berangère Lebas ,&nbsp;Anouck Habrant ,&nbsp;Grégoire Malandain ,&nbsp;Gabriel Paës ,&nbsp;Yassin Refahi","doi":"10.1016/j.dib.2025.111618","DOIUrl":"10.1016/j.dib.2025.111618","url":null,"abstract":"<div><div>The transition to use plant cell walls as an alternative to fossil carbon resources is important in the context of climate change. To achieve an economically viable plant cell wall transformation into biofuels and biomaterials, it is essential to better understand cell wall enzymatic deconstruction and overcome its recalcitrance to deconstruction. While identification of nanoscale markers of recalcitrance has been the focus of the majority of studies, quantitative investigation of cell wall hydrolysis at microscale, particularly the cell wall morphological parameters, remains relatively insufficiently addressed. This is mainly due to the lack of quantitative data on cell wall enzymatic deconstruction at microscale. Acquisition and processing of reliable microscale datasets are notoriously challenging; the sample needs to be kept at a constant temperature for efficient enzymatic hydrolysis and imaged over a considerable number of hours. Processing the acquired datasets to extract cell wall morphological parameters is also challenging due to cell wall deconstruction and deformations occurring during enzymatic hydrolysis. This becomes particularly challenging under high deconstruction conditions. The datasets presented here include time-lapse 3D images of highly deconstructed pretreated spruce wood acquired using fluorescence confocal microscopy, together with cell resolution segmentations of the acquired time-lapses. Along with this hydrolysis dataset, control time-lapse images of pretreated spruce wood samples acquired without adding enzymatic cocktail are also presented. The control dataset includes 6505 segmented and tracked cells. The hydrolysis dataset includes 6699 tracked cells at various stages of extensive deconstruction. Overall, these datasets provide a reliable and comprehensive set of time-lapse 3D images to study cell wall enzymatic deconstruction at cell and tissue scales, which can be used to better understand the microscale limiting factors of efficient transformation of plant biomass into sustainable products.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111618"},"PeriodicalIF":1.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941537","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 image dataset of plum leaf and fruit for disease classification","authors":"Rejowan Arifin Nayeem,&nbsp;S.M. Abdullah Al Muhib,&nbsp;Shahriar Marjan,&nbsp;Md Hasan Imam Bijoy,&nbsp;Md Assaduzzaman","doi":"10.1016/j.dib.2025.111625","DOIUrl":"10.1016/j.dib.2025.111625","url":null,"abstract":"<div><div>Plums, commonly known as Indian jujube, are economically important, valued for nutritional benefits and consumed by people from all over the world. The development of a comprehensive Plum leaf and fruit dataset is highly essential for advancing agricultural research and enabling effective disease management systems using machine learning techniques. This dataset serves as a foundational resource for machine learning based classification and bridges the gap between agricultural research and computer vision to support automated disease detection and fruit quality assessment. Researchers will be able to utilize this dataset to implement early disease detection which leads to improve crop management and supply quality and reduce the usage of chemicals. Proper utilization of this dataset can help farmers to reduce financial losses and encourage sustainable farming practices. The dataset was collected between December 2024 and February 2025 under various environmental conditions. It consists of 3,554 original images, an equal number of processed images and 18,000 augmented images generated from the original dataset. The dataset is categorized into six distinct classes: Shot Hole, Bacterial Spot, Wilted Leaf, Healthy Leaf, Unhealthy Plum, and Healthy Plum. This dataset contributes significantly to advance deep learning in agriculture enabling early disease detection and fruit quality monitoring.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111625"},"PeriodicalIF":1.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070218","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":"Genetic marker data for sweetpotato improvement","authors":"Imana L. Power ,&nbsp;Brian E. Scheffler ,&nbsp;Xiaofen F. Liu ,&nbsp;Sheron A. Simpson ,&nbsp;Linda L. Ballard ,&nbsp;Marshall C. Lamb ,&nbsp;Renee S. Arias","doi":"10.1016/j.dib.2025.111630","DOIUrl":"10.1016/j.dib.2025.111630","url":null,"abstract":"<div><div>A total of 768 molecular markers were developed for <em>Ipomoea batatas</em> (L.) Lam., consisting of 689 simple sequence repeats (SSRs) and 79 single nucleotide polymorphisms (SNPs). All the markers were distributed across the sweet potato genome, averaging 51 markers per chromosome. The markers were tested on DNA samples from five cultivars of <em>I. batatas</em>, assessing their amplification efficiency and polymorphism. Here we provide the primer sequences tested, their chromosome locations, the analysis including amplicon sizes, and highlight 92 that showed polymorphism between Beauregard and Tanzania. This dataset offers valuable resources for constructing high-resolution linkage maps and facilitates advanced genetic studies and breeding programs in sweetpotato.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111630"},"PeriodicalIF":1.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070215","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 structured-illumination reflectance imaging dataset for woody breast assessment of broiler meat","authors":"Yuzhen Lu ,&nbsp;Hamed Sardari","doi":"10.1016/j.dib.2025.111612","DOIUrl":"10.1016/j.dib.2025.111612","url":null,"abstract":"<div><div>Wood breast (WB) myopathy is an economically important muscular defect that downgrades poultry meat quality and currently requires manual assessment for identifying and removing affected products at processing lines. An image dataset was created to assess WB conditions in broiler breast fillets using structured-illumination reflectance imaging (SIRI) as a non-destructive, objective means for WB assessment. A custom-assembled SIRI platform was used for sample imaging, and it mainly consisted of a broadband quartz tungsten halogen light source, a digital micro-mirror-device-based projector that shined phase-shifted sinusoidal patterns of light over samples, a monochromatic camera with a resolution of 2048 × 2048 pixels, and a computer, operating in an enclosed chamber. A total of 168 broiler breast fillets were collected from a commercial poultry processing plant and categorized by trained personnel into 72 “Normal” (WB-free) and 96 “Defective” (WB-affected) fillets based on tactile palpation and visual inspection. Sinusoidal illumination patterns at eight different spatial frequencies (0.015–0.150 cycles/mm) were sequentially projected onto the samples, and the reflectance pattern images were captured under the sinusoidal illumination of three phase-shifted patterns at each spatial frequency, yielding a set of 24 images acquired per sample. Hence the dataset consists of a total of 4032 raw pattern images, each of which is of size 2048 × 2048 pixels and saved as a 16-bit grayscale image in .tif format. Through demodulation, direct component (DC), amplitude component (AC), and phase difference images can be readily obtained from the three phase-shifted raw pattern images at each spatial frequency, and these images, especially the phase difference image that depicts the surface geometry, are useful for WB assessment and sample classification. In addition to the raw pattern images, the demodulated image (DC, AC, and phase difference) data is also included in the dataset. This dataset represents the first publicly available SIRI dataset and is expected to be a valuable resource for advancing SIRI for poultry quality assessment and beyond.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111612"},"PeriodicalIF":1.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941534","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":"How can we make GPS tracking studies more open, reproducible, and collaborative? A vision for the OpenGPS platform","authors":"Milad Malekzadeh ,&nbsp;Hui Jeong Ha ,&nbsp;Katarzyna Sila-Nowicka ,&nbsp;Vanessa Brum-Bastos ,&nbsp;Jinhyung Lee ,&nbsp;Urška Demšar ,&nbsp;Jed A. Long","doi":"10.1016/j.dib.2025.111603","DOIUrl":"10.1016/j.dib.2025.111603","url":null,"abstract":"<div><div>This paper introduces OpenGPS, a platform envisioned for the archiving and processing of human mobility GPS data. The OpenGPS addresses the need for a centralized, privacy-preserving system that securely stores, shares, and analyzes GPS tracking datasets. The platform is envisioned to develop in three phases. Phase I focuses on collecting metadata from existing GPS tracking studies worldwide, providing a foundation for future research. Phase II involves archiving GPS data with standardized formats and robust privacy safeguards, ensuring data is accessible while protecting individual privacy. Phase III integrates advanced analytical tools and workflows directly into the platform, enabling efficient analysis and fostering collaboration among researchers. The OpenGPS aims to overcome the limitations of current human mobility studies by offering a standardized repository that enhances reproducibility and openness in research. By facilitating the sharing of data and methodologies, the OpenGPS will promote new insights and innovations in human mobility research. This platform is poised to become a critical resource for the scientific community, bridging gaps in data availability, and enabling comprehensive meta-analyses across different geographical and temporal scales. Through OpenGPS, researchers can collaborate more effectively, share resources, and advance the understanding of human mobility patterns globally.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111603"},"PeriodicalIF":1.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941536","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":"IBERBIRDS: A dataset of flying bird species present in the Iberian Peninsula","authors":"Paula Rodríguez ,&nbsp;Rubén Parte ,&nbsp;Guillermo A. González ,&nbsp;Alejandra Gacho ,&nbsp;Darío Santos ,&nbsp;Rubén Usamentiaga ,&nbsp;Oscar D. Pedrayes","doi":"10.1016/j.dib.2025.111610","DOIUrl":"10.1016/j.dib.2025.111610","url":null,"abstract":"<div><div>Advancements in computer vision and deep learning have transformed ecological monitoring and species identification, enabling automated and accurate data labelling. Despite these advancements, robust AI-driven solutions for avian species recognition remain limited, primarily due to the scarcity of high-quality annotated datasets. To address this gap, this article introduces IBERBIRDS—a comprehensive and publicly accessible dataset specifically designed to facilitate automatic detection and classification of flying bird species in the Iberian Peninsula under real-world conditions.</div><div>The dataset comprises 4000 images representing 10 ecologically significant medium to large-sized bird species, with each image annotated using bounding box coordinates in the YOLO detection format. Unlike existing datasets that typically feature close-up or ideal-condition imagery, IBERBIRDS focuses on mid-to-long range photographs of birds in flight, providing a more realistic and challenging representation of scenarios commonly encountered in birdwatching, conservation, and ecological monitoring. Images were sourced from publicly available, expert-validated ornithology platforms and underwent rigorous quality control to ensure annotation accuracy and consistency. This process included homogenizing color profiles and formats, as well as manual refinement to ensure that each image contains a single bird specimen. Additionally, detailed provenance and taxonomic metadata for each image has been systematically integrated into the dataset.</div><div>The lack of pre-annotated datasets has significantly restricted large-scale ecological analysis and the development of automated techniques in avian research, hindering the progress of AI-driven solutions tailored for bird species recognition. By addressing this gap, this dataset serves as a comprehensive benchmark for avian studies, fostering advancements in various applications such as conservation initiatives, environmental impact assessments, biodiversity preservation strategies, real-time tracking systems, and video-based analysis. Additionally, IBERBIRDS constitutes a resource for computer vision applications, supporting educational programs tailored to ornithologists and birdwatching communities. By openly providing this dataset, IBERBIRDS promotes scientific collaboration and technological advancements, ultimately contributing to the preservation and understanding of avian biodiversity.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111610"},"PeriodicalIF":1.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927366","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":"PeruFoodNet: A unique dataset of traditional peruvian food for image recognition systems and allergenic ingredient inference","authors":"María Franchesca Arzola Gutierrez,&nbsp;Edgar Alexander Canchari Muñoz,&nbsp;Edwin Jonathan Escobedo Cárdenas","doi":"10.1016/j.dib.2025.111604","DOIUrl":"10.1016/j.dib.2025.111604","url":null,"abstract":"<div><div>Peruvian cuisine has won numerous international awards, attracting tourists from around the world to Peru to experience its diverse culinary offerings. However, some dishes contain ingredients that can trigger allergic reactions, posing a potential health risk for visitors. To address this, we created PeruFoodNet, a dataset featuring 4,000 images of traditional Peruvian dishes. The dataset includes 40 of the most popular dishes, such as Ceviche and Anticuchos, with 100 images of each dish. The images of the dishes have been captured from various angles, settings, lighting conditions, dimensions and backgrounds. To gather these images, we prepared the dishes ourselves, purchased some from restaurants, and received contributions from external users over a two-month period. However, most of the images were captured by the authors of the dataset. The dataset is publicly available and can be valuable for research in image recognition and classification using Computer Science techniques, such as Deep Learning. Additionally, it can aid in identifying allergenic ingredients in dishes by linking the dish’s image to a list of ingredients through a technological platform, such as a chatbot or an app.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111604"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941538","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":"GastroEndoNet: Comprehensive endoscopy image dataset for GERD and polyp detection","authors":"Abu Kowshir Bitto ,&nbsp;Md. Hasan Imam Bijoy ,&nbsp;Kamrul Hassan Shakil ,&nbsp;Aka Das ,&nbsp;Khalid Been Badruzzaman Biplob ,&nbsp;Imran Mahmud ,&nbsp;Syed Md. Minhaz Hossain","doi":"10.1016/j.dib.2025.111572","DOIUrl":"10.1016/j.dib.2025.111572","url":null,"abstract":"<div><div>The gastrointestinal (GI) system is fundamental to human health, supporting digestion, nutrient absorption, and waste elimination. Disruptions in GI function, such as Gastroesophageal Reflux Disease (GERD) and gastrointestinal polyps, can lead to significant health complications if not diagnosed and managed early. However, manual interpretation of endoscopic images is time-consuming and prone to human error, highlighting the need for automated diagnostic tools. In this study, we introduce a comprehensive dataset of 24,036 high-quality endoscopic images, categorized into four classes: GERD, GERD Normal, Polyp, and Polyp Normal. This dataset is designed to facilitate research in automated detection and classification of these conditions through machine learning algorithms. The dataset consists of 4006 primary images collected following endoscopic procedures, which were augmented using six distinct techniques, expanding the total number of images to 24,036. It includes 5844 images of GERD cases (974primary images), 6618 images of GERD Normal (1103 primary images), 4674 images of Polyps (779 primary images), and 6900 images of Polyp Normal (1150 primary images). These images, pre-processed and resized to a resolution of 512 × 512 pixels, were obtained from Zainul Haque Sikder Women’s Medical College &amp; Hospital (Pvt.) Ltd. and saved in JPG format. This dataset addresses a critical gap in the availability of large, diverse, and well-labelled medical image datasets for training AI-driven healthcare solutions. It provides an invaluable resource for developing machine learning models aimed at the automatic diagnosis, classification, and detection of GERD and polyps, potentially improving the speed and accuracy of clinical decision-making. By leveraging this dataset, researchers can contribute to enhanced diagnostic tools that could significantly improve healthcare outcomes and patient quality of life in the field of gastroenterology.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111572"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927485","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":"An open-source and spatially diverse synthetic population dataset for agent-based modelling and microsimulation in Ireland","authors":"Seán Caulfield Curley,&nbsp;Karl Mason,&nbsp;Patrick Mannion","doi":"10.1016/j.dib.2025.111611","DOIUrl":"10.1016/j.dib.2025.111611","url":null,"abstract":"<div><div>Spatial microsimulations, where simulation units represent people or households in a small area, are extremely useful for modelling a wide range of socio-economic scenarios at a fine scale. The characteristics of individuals in these simulations' populations need to accurately represent the real characteristics of the target area to model realistic scenarios. However, individual-level data is not available for the vast majority of populations, Ireland included, due to privacy concerns. Thus, a representative synthetic population for the Republic of Ireland is needed. The data from four methods of generating synthetic populations at the Electoral Division level are given in this paper. Realistic individuals are created by sampling from the Central Statistics Office (CSO) Labour Force Survey. Spatial heterogeneity is achieved by matching the aggregate counts of individuals' characteristics to those from the CSO Census Small Area Population Statistics. Individuals are assigned six characteristics: age group, sex, marital status, house size, primary economic status, and highest level of education achieved.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111611"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070217","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}