Data in Brief最新文献

{"title":"Benchmarking raw datasets and collaboratively-evolving processed data for markerless motion capture analysis.","authors":"Antoine Muller, Alexandre Naaïm, Raphaël Dumas, Thomas Robert","doi":"10.1016/j.dib.2025.112044","DOIUrl":"10.1016/j.dib.2025.112044","url":null,"abstract":"<p><p>We present a dataset designed for benchmarking markerless motion capture methods (from videos to joint kinematics). The dataset includes both raw and processed data. Two participants performed five tasks - walking, sit-to-stand, manual material handling, handstand hold or Y-pose (depending on the participant), and a jointly performed dance sequence. Movements were captured simultaneously recorded using 10 optoelectronic cameras (Qualisys Miqus M3, 120 Hz) and 9 video cameras (Qualisys Miqus Video, 60 Hz, 1920×1088 pixels). The raw dataset provides 3D marker trajectories and video recordings. The processed dataset includes joint kinematics obtained from both marker-based motion capture and 7 different markerless methods, contributed by multiple research teams as part of a challenge organized during a national biomechanics seminar. Additionally, the open-access GitHub repository containing processed data enables researchers to contribute new markerless methods estimated and expand the dataset collaboratively. This resource aims to facilitate benchmarking and support the development of robust markerless motion analysis methods.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112044"},"PeriodicalIF":1.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198830","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":"PY-CrackDB: A pavement crack dataset from paraguayan roads for context-aware computer vision models","authors":"Fredy Gabriel Ramírez-Villanueva ,&nbsp;José Luis Vázquez Noguera ,&nbsp;Horacio Legal-Ayala ,&nbsp;Julio César Mello-Román ,&nbsp;Pastor Enmanuel Pérez-Estigarribia","doi":"10.1016/j.dib.2025.112060","DOIUrl":"10.1016/j.dib.2025.112060","url":null,"abstract":"<div><div>PY-CrackDB, a novel dataset of asphalt pavement images designed for developing context-aware artificial intelligence systems. The dataset contains 569 images (351 × 500 pixels), collected from national routes near Coronel Oviedo, Paraguay, and divided into 369 images with cracks and 200 without. A primary contribution of this work is its specific focus on fine fissures (&lt; 3 mm wide), a category critical for early-stage maintenance according to Paraguayan road engineering standards. Data collection was performed under standardized conditions, and all annotations were created by civil engineering professionals and subsequently verified through a rigorous cross-review protocol to ensure accuracy. This methodological rigor resulted in a dataset that is particularly suitable for training and validating models for semantic segmentation and early defect detection, ultimately supporting the development of preventative road maintenance strategies.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"63 ","pages":"Article 112060"},"PeriodicalIF":1.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128351","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":"Experimental dataset on the thermo-mechanical response of an energy piled wall section","authors":"Luis Villegas ,&nbsp;Raul Fuentes ,&nbsp;Guillermo A. Narsilio","doi":"10.1016/j.dib.2025.112064","DOIUrl":"10.1016/j.dib.2025.112064","url":null,"abstract":"<div><div>The investigation of geotechnical structures as heat exchangers for ground source heat pump (GSHP) systems has been extensive, mainly aiming at reducing initial capital costs of implementing renewable energy sources for space heating and cooling. Despite advancements in understanding the thermo-mechanical response of energy pile foundations, industry adoption lags behind academic developments. To bridge this gap and broaden the application of this technology to other geotechnical structures, there is a need for reliable, easy-to-use methods and confidence building through demonstration projects. This work presents a thermo-mechanical dataset from a pilot project of three energy piles within a retaining wall system at a Metro station in Melbourne, Australia. The data encompasses heat carrier fluid flow rate and temperature, station (air) temperature, soil temperatures at two depths, and temperature and uniaxial strains at different depths and locations across two piles. Unlike previous datasets focused on single piles, this dataset captures both single and multiple (three) simultaneously activated piles within a piled retaining wall, providing a better representation of an energy wall system. Beyond the raw data interpretation and model validation, the dataset can be used to evaluate the system's thermal performance and develop analytical methods needed in the industry.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"63 ","pages":"Article 112064"},"PeriodicalIF":1.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218701","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 dataset of healthcare road accessibility in Australia: service-based grouping and residential reachability.","authors":"Kiki Adhinugraha, Thanh Phan, Richard Beare, Albert Phan, Shiyang Lyu, David Taniar","doi":"10.1016/j.dib.2025.112068","DOIUrl":"10.1016/j.dib.2025.112068","url":null,"abstract":"<p><p>This dataset provides national coverage of healthcare road accessibility in Australia, focusing on five types of facilities: four hospital groups based on clinical service availability and one group of ambulance stations. Hospitals are classified into four groups based on the number of clinical services they offer: Blue (>16 services), Green (11-15), Orange (5-10), and Red (<5). Ambulance stations are treated as a distinct group to support emergency access analysis. For each group, the dataset presents travel distance and estimated driving time from major road points, enabling a consistent and structured view of spatial access to healthcare services across the country. Road accessibility is calculated using shortest-path routing across OpenStreetMap road networks. Estimated travel times are adjusted based on road speed limits and regional driving conditions (urban, regional, or remote). Two accessibility layers are provided: (1) a comprehensive set of road nodes showing distance (in kilometres) and travel time (in hours) to the nearest hospital in each group, and (2) a set of sampled residential address points, representing population perspectives, with linked nearest-healthcare metrics for each group, and aggregated summaries by SA2, LGA, and remoteness levels. Travel time accuracy was validated against Google Maps API, with average discrepancies of 3 min in urban areas, 10 min in regional areas, and 90 min in remote areas. Summary statistics are also available at the Local Government Area (LGA), Statistical Area Level 2 (SA2), and remoteness levels, allowing high-level regional comparisons. The dataset includes vector-based geospatial files representing hospitals, ambulance stations, road nodes, and residential samples. All layers use the GDA2020 datum (EPSG:7844) and are derived from the Australian Institute of Health and Welfare (hospital services), Digital Atlas Australia (ambulance locations), OpenStreetMap (road networks), Australian Bureau of Statistics (boundaries), and Geoscape G-NAF (residential addresses). The dataset supports public health planning by illustrating which facilities are reachable by road, the estimated distance and travel time to reach them, and how hospital service capacity influences spatial accessibility.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112068"},"PeriodicalIF":1.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12510037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279186","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":"Mapping four decades of lake chlorophyll-a across a continental watershed: A dataset for the lake winnipeg basin (1984-2023).","authors":"Sassan Mohammady, Irena F Creed","doi":"10.1016/j.dib.2025.112035","DOIUrl":"10.1016/j.dib.2025.112035","url":null,"abstract":"<p><p>We present a standardized pan-watershed dataset of annual chlorophyll-a concentration (Chl-a) in 27,313 lakes (≥ 10 ha) draining into Lake Winnipeg, Canada, spanning 1984-2023. Lake polygons from HydroLAKES were integrated with Landsat 5/7/8 Collection 2 imagery processed in Google Earth Engine (GEE) using a reproducible workflow that (1) filters July-October scenes (peak phytoplankton biomass season), (2) masks non-water pixels from each scene, (3) converts Landsat digital numbers to surface reflectance values, (4) applies a cross-sensor Chl-a retrieval model calibrated against in-situ samples, (5) calculates the spatial mean of Chl-a in each lake for each scene, and (6) calculates the median value of all spatial-mean values per lake per year. Outputs include per-lake annual Chl-a provided as both natural-log and back-transformed Chl-a (µg L⁻¹) plus annual trophic state classes delivered in an Excel workbook and two geodatabases for mapping. The accompanying annotated GEE and R codes, input lake boundaries, and documentation enable transparent reuse and straightforward adaptation to other regions, time periods, or sensors. This resource fills a critical monitoring gap for an agriculturally influenced, bloom-prone continental watershed and supports research and management by establishing productivity baselines, detecting departures from historical conditions, and assessing bloom timing at scales relevant to decision-making. All data, inputs, and code are openly available via Zenodo.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112035"},"PeriodicalIF":1.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198765","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":"Kazakhstani HER2 breast cancer digital image dataset: The ADEL dataset.","authors":"Gauhar Dunenova, Aidos Sarsembayev, Alexandr Ivankov, Dilyara Kaidarova, Zhanna Kalmatayeva, Elvira Satbayeva, Natalya Glushkova","doi":"10.1016/j.dib.2025.112052","DOIUrl":"10.1016/j.dib.2025.112052","url":null,"abstract":"<p><p>Breast cancer remains a leading cause of cancer-related mortality among women worldwide, with HER2-positive subtypes requiring precise diagnostic approaches to guide targeted therapy. Digital pathology and AI-based tools offer promising solutions, but their development relies heavily on high-quality digital datasets, labelled or annotated. In this study, we present a dataset of digital images of breast cancer tissue samples with immunohistochemical expression of human epidermal growth factor receptor 2 (HER2) classes 0, 1+, 2+, and 3+. Breast cancer tissue samples were formalin-fixed and paraffin-embedded (FFPE), followed by the preparation of paraffin blocks and 5-µm sections. Immunohistochemical staining was performed using a Ventana Benchmark Ultra automated immunostainer with PATHWAY anti-HER2/neu (4B5) rabbit monoclonal antibodies and ULTRA VIEW detection system. Digital images were acquired via a fully automated digital system (KFB PRO 120 scanner) at INVIVO LLP with 40x magnification and one focusing layer, ranging in size from 50 MB to 2 GB, depending on the size of the tissue sample fixed on the original slide. The dataset consists of 418 subfolders with images, each corresponding to a source image and containing a different number of tiles depending on the size of the source image. The original images were preprocessed using a conversion script that transformed SVS files into sub-images with a 1:1 aspect ratio in JPEG format. A non-overlapping sliding window approach was applied to generate these sub-images, optimized for machine learning applications. A square window of 1000 × 1000 pixels was used to crop sub-images with a 1:1 aspect ratio. The stride of the sliding window was set to a value that was a multiple of the image resolution (as determined during preprocessing). As a result, a variable number of sub-images were generated from each original SVS image, depending on its size. The output file format was JPEG. Clinical labeling of the data was provided by reference laboratory pathologists with expertise in advanced oncological morphology evaluations. This dataset allows training and validation of machine learning models for the diagnosis, recognition, and classification of breast cancer using the available labeling, as well as for educational purposes for residents and pathologists.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112052"},"PeriodicalIF":1.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198813","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 for exploring relation between sound and cutting forces components in longitudinal turning of C45E steel.","authors":"Vladimir Mitrović, Milan Zdravković, Milan Trifunović, Miloš Madić, Predrag Janković","doi":"10.1016/j.dib.2025.112051","DOIUrl":"https://doi.org/10.1016/j.dib.2025.112051","url":null,"abstract":"<p><p>Monitoring cutting force components (tangential, radial, and axial) in longitudinal turning helps identifying unfavourable cutting conditions. However, force sensor can be a costly investment, in addition to being technically challenging to integrate into the machine tool. The primary purpose of this data repository is to provide a way to evaluate the potential that sound could hold for estimating cutting force components, with the idea to possibly simplify the monitoring system. Data was collected by monitoring sound and cutting force components during longitudinal turning of C45E steel. In total, 100 experiment trials were carried out with different settings of cutting speed, depth of cut and feed rate. The sensory data consists of raw sound recordings and measurements of cutting force components, for each experiment trial. In addition, datasets with extracted sound and force features are provided, along with code used for this purpose. The sound features dataset is particularly extensive, including 260 extracted sound features in time and frequency domain. Both feature extraction process and initial exploratory data analysis are presented, making a base ground for further analysis. The researchers in manufacturing engineering, acoustics and other relevant fields can either use datasets with extracted features for conducting analysis or use only raw data and compile their own methodology for feature extraction and analysis.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112051"},"PeriodicalIF":1.4,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12493221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145231784","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":"3D human pose point cloud data of light detection and ranging (LiDAR)","authors":"Farah Zakiyah Rahmanti ,&nbsp;Moch. Iskandar Riansyah ,&nbsp;Oddy Virgantara Putra ,&nbsp;Eko Mulyanto Yuniarno ,&nbsp;Mauridhi Hery Purnomo","doi":"10.1016/j.dib.2025.112043","DOIUrl":"10.1016/j.dib.2025.112043","url":null,"abstract":"<div><div>3D Light Detection and Ranging (LiDAR) sensors are closely related to computer vision and deep learning. 3D LiDAR sensors are commonly embedded in smart vehicles to segment humans, cars, trucks, motors, and other objects. However, 3D LiDAR can also be used indoors to predict human poses that are more friendly to a person's privacy because 3D LiDAR does not capture facial images, but it produces data in the form of point clouds. The point cloud produces spatial, geometric, and temporal information which can be used to predict, detect, and classify human poses and activities. The data output from 3D LiDAR, which includes spatial and temporal data, is in PCAP (.pcap) and JSON (.json) formats. The PCAP file contains the sequence frame of the 3D human pose point cloud, and the JSON file contains the metadata. Each human pose class label has one PCAP file and one JSON file. The raw spatio-temporal data must be processed into PCD format as a 3D human pose point cloud dataset for each human pose.</div><div>The total human pose dataset is 1400 3D point cloud data with PCD format (.pcd) used for the training and testing process in deep learning, consisting of four human pose labels. The label classes are hands-to-the-side, sit-down, squat-down, and stand-up human poses, with each class having 280 3D point cloud data used as training data. While the test data amounted to 280 3D point cloud data. The data collection process uses 3D LiDAR, a tripod, a personal computer/laptop, and a talent, demonstrating basic human poses. The 3D LiDAR used is OS1, a product of Ouster, which has a range of 90–200 m, 128 channels of resolution, and a temperature of -40 – 60° C. For talent, there is one person and male gender in this current shooting. However, in its development, it can also take female or children or elderly talent to enrich the human pose dataset. The talent is between 30 and 40 years old. The distance between the 3D LiDAR and the talent position is 120 cm. Data collection took place from 10:00 a.m. to 1:00 pm. indoors.</div><div>This dataset is used for human pose prediction using one of the deep learning algorithms, Convolutional Neural Network (CNN). However, the developers can also use other deep learning algorithms such as transformers, Graph Neural Network (GNN), etc.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112043"},"PeriodicalIF":1.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095162","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":"Transcriptome datasets of wheat plant cultures treated with seed priming fertilizer.","authors":"Kincső Decsi, Mostafa Ahmed, Roquia Rizk, Donia Abdul-Hamid, Zoltán Tóth","doi":"10.1016/j.dib.2025.112050","DOIUrl":"10.1016/j.dib.2025.112050","url":null,"abstract":"<p><p>Extreme weather conditions cause stress in agriculture. To prevent these stress effects, techniques are now available that focus on prevention. One of these solutions could be the use of priming agents, which prepare plants for stress effects, as their application can trigger plant defense responses. Wheat plants were treated with seed priming fertilizer in advance and then exposed them to drought stress during the growing season. Deep RNA sequencing was performed using the NGS technique, which sequences - after processing and evaluation - are suitable for examining the stimulatory effects of seed priming and for monitoring the long-term effects of priming during stress effects. Four forward and reverse SRA data sets were deposited in NCBI. In addition, a file - containing the <i>de novo</i> transcriptome assembled from the cleaned and filtered raw reads (Transcriptome Shotgun Assembly - TSA) and the count table from the RNA-sequencing read quantification (Count table) - were deposited in the Mendeley database. Future results from the databases will be suitable for the immediate (priming) and long-term (under stress conditions) study of the effects of treatments and will allow the identification and monitoring of stimulated plant biochemical processes - based on gene expression changes.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112050"},"PeriodicalIF":1.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184980","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":"HRI-confusion: A multimodal dataset for modelling and detecting user confusion in situated human-robot interaction","authors":"Na Li ,&nbsp;Jane Courtney ,&nbsp;Robert Ross","doi":"10.1016/j.dib.2025.112047","DOIUrl":"10.1016/j.dib.2025.112047","url":null,"abstract":"<div><div>The dataset was collected from 28 participants (17 female, 9 male, and 1 non-binary) for a study aimed at modelling and detecting user social behaviours with different confusion states in task-oriented situated human-robot interaction (HRI). The dataset consists of user facial body video recordings synchronised with user speech across three designed experiment scenarios (Tasks 1 - 3). Each experiment lasted approximately one hour per participant. The videos are segmented into individual clips corresponding to specific experimental conversations under predefined conditions: general confusion and non-confusion for Task 1 and 3; and productive confusion, unproductive confusion, and non-confusion for Task 2.</div><div>In total, the dataset contains 789 video clips (body: 392, face: 397). Each video is recorded in high-definition RGB format, capturing user facial expressions or body language along with their speech. These multimodal data provide a valuable resource for studying user cognitive and mental states in human-robot interaction and human-computer interaction.</div><div>The data collected for Task 2 was used in [9]. In compliance with GDPR (General Data Protection Regulation) and DPIA (data protection impact assessment) guidelines, the dataset is freely available upon request at <span><span>https://sites.google.com/view/hridatarequst/home</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112047"},"PeriodicalIF":1.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095158","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}