Data in Brief最新文献

{"title":"A collection of experimental data from a multiphase plant simulating oil and gas transport","authors":"Giovanni Mazzuto,&nbsp;Ilaria Pietrangeli,&nbsp;Marco Ortenzi,&nbsp;Vincenzo Foti,&nbsp;Filippo Emanuele Ciarapica,&nbsp;Maurizio Bevilacqua","doi":"10.1016/j.dib.2025.112038","DOIUrl":"10.1016/j.dib.2025.112038","url":null,"abstract":"<div><div>This paper details the data gathered from an experimental plant designed to simulate an oil and gas transportation system. The plant was configured to replicate various working conditions, including both normal and anomalous operational states. Different scenarios were created to reflect realistic conditions encountered in oil and gas transportation, such as steady-state operations, typical operational fluctuations, and a range of anomalies like leakages, obstructions, and working parameter modifications.</div><div>The data provide a comprehensive view of the plant behaviour under these diverse conditions. This information can be instrumental for applications in machine learning, where it can aid in the development of predictive maintenance algorithms and anomaly detection models. In the realm of control theory, the data can support the design and validation of advanced control strategies to ensure efficient and safe operations. Additionally, the data enhance process comprehension, offering insights into the mechanisms governing oil and gas transportation systems.</div><div>This dataset can be helpful for the analysis of the dynamic responses of an oil and gas transportation system under various operational scenarios. The structured data can be used to model and simulate system behaviour, providing a foundation for improving process resilience, efficiency and reliability. The information captured in this dataset can be useful for improving theoretical and practical understanding in the fields of machine learning, control theory, and process engineering.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112038"},"PeriodicalIF":1.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095161","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":"VNWoodKnot: A benchmark image dataset for wood knot detection and classification","authors":"Vinh Tran,&nbsp;Duy Lam,&nbsp;Tuong Le","doi":"10.1016/j.dib.2025.112039","DOIUrl":"10.1016/j.dib.2025.112039","url":null,"abstract":"<div><div>Timber knot detection is essential for automated grading and quality control in the wood processing industry. Knots, which arise at the intersection of branches and the tree trunk, are among the most influential defects affecting both structural integrity and aesthetics. This paper introduces VNWoodKnot, a publicly available image dataset comprising 1,515 high-resolution wood surface images, collected in a Vietnamese industrial facility. The dataset includes three categories: live knots (519 images), dead knots (496 images), and knot-free surfaces (500 images). Live knots are structurally integrated and color-consistent, while dead knots are darker, cracked, and loosely attached. VNWoodKnot enables both classification and object detection tasks and addresses a critical gap in publicly accessible datasets for AI-driven wood defect inspection. It serves as a crucial benchmark for the development of real-time, scalable, and reliable deep learning models for industrial-grade wood defect inspection.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112039"},"PeriodicalIF":1.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095159","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":"Norwegian energy community dataset: An electricity-hydrogen system with renewables, battery storage & hydrogen demand.","authors":"Pratik Mochi, Magnus Korpås","doi":"10.1016/j.dib.2025.112042","DOIUrl":"10.1016/j.dib.2025.112042","url":null,"abstract":"<p><p>This paper presents the Norwegian Energy Community Dataset, developed to support modelling and optimization of integrated electricity-hydrogen energy systems. The dataset represents a local energy community in Porsgrunn, Norway, consisting of 400 electricity end users, including residential, commercial, and industrial consumers and prosumers. It includes hourly smart meter data for electricity consumption and generation over a nine-month period (January to September 2024). The dataset features distributed energy resources such as rooftop solar PV (installed by 300 users), a 1.3 MW wind power plant and battery energy storage systems for 250 users. Electricity market data, including hourly buy and sell prices for 2024, are also included. On the hydrogen side, the dataset incorporates transport demand from (a) a real hydrogen-powered ferry operating in a northern Norwegian island and (b) 15 synthetic hydrogen buses modelled using Norwegian specifications and realistic operational patterns. Green hydrogen production is modelled using two electrolyzers (1.2 MW and 2.5 MW) along with associated hydrogen storage. The dataset would enable researchers to explore sector coupling, local energy market design, flexibility strategies and cost optimization of community scale integrated energy system.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112042"},"PeriodicalIF":1.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145185008","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":"Whole genome short read data from 567 bulls of 14 breeds provides insight into genetic diversity of French cattle.","authors":"Mekki Boussaha, Camille Eché, Christophe Klopp, Cécile Grohs, Marine Milhes, Amandine Suin, Tabatha Bulach, Rachel Fourdin, Thomas Faraut, Claire Kuchly, Sébastien Fritz, Caroline Vernette, Maulana Naji, Valentin Sorin, Aurélien Capitan, Christine Gaspin, Denis Milan, Didier Boichard, Carole Iampietro, Cécile Donnadieu","doi":"10.1016/j.dib.2025.112049","DOIUrl":"10.1016/j.dib.2025.112049","url":null,"abstract":"<p><p>Technological developments in high-throughput sequencing and advances in bioinformatic analysis allowed to sequence and study a very large number of genomes from a single species (cattle). Analyzing this data set enabled to generate the corresponding genomic variant database, especially for single nucleotide polymorphisms (SNPs) and small insertion or deletion (Indels) variations. These variants and genotypes allowed to better characterize the genetic diversity of these breeds. In this work, we sequenced 567 bulls from 14 different breeds (Holstein, Montbéliarde, Normande, Brown Swiss, Simmental, Abondance, Tarentaise, Vosgienne, Blonde d'Aquitaine, Charolaise, Limousine, Aubrac, Flamande, Parthenaise). Each sample was sequenced at an approximately 15x depth on the Illumina Novaseq6000 platform. We detected 34,252,080 variants, 25,115,987 of which were already known in the Ensembl variation database version 110 and 9,136,093 were absent and were considered as novel variants. This data set represents a useful resource for the community to better identify SNPs or indels such as mutation anticipation and provides new insights into bovine genetic diversity.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112049"},"PeriodicalIF":1.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145205815","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":"A benchmark dataset of electrical signals from a permanent magnet synchronous generator for condition monitoring.","authors":"Rafael Noboro Tominaga, Santiago Silveira Barbosa, Luan Andrade Sousa, Angelo Dos Santos Lunardi, Rodolfo Varraschim Rocha, Sérgio Luciano Ávila, Bruno Souza Carmo, Renato Machado Monaro, Maurício Barbosa de Camargo Salles","doi":"10.1016/j.dib.2025.112040","DOIUrl":"10.1016/j.dib.2025.112040","url":null,"abstract":"<p><p>The proper monitoring of sensitive components in rotating electrical machines plays a critical role in preventing internal faults that may lead to irreversible damage and unplanned shutdowns. Offshore wind power generation is increasingly adopting permanent magnet synchronous generators (PMSGs) because of their high efficiency and low maintenance requirements. However, internal short-circuit faults remain a challenge and require effective fault detection strategies. Inter-turn and inter-winding faults, in particular, may not cause immediate damage but can evolve over time, leading to severe equipment failures. These failures may require generator shutdowns, resulting in significant financial and operational losses. This dataset provides high-resolution electrical measurements from a PMSG under healthy and faulty conditions, supporting the development and validation of related diagnostic and control strategies. We collected data from a laboratory test bench that allows controlled insertion of internal faults, such as short-circuits between turns and windings. The generator, connected to the grid via a power converter, was monitored using an Imperix B-Box RCP system loaded with control algorithms developed in Simulink. Signals were sampled at 20 kHz and recorded through the Imperix Cockpit, with each test lasting three seconds and capturing pre-fault, fault, and post-fault conditions. This structure enables users to study transient responses, steady-state behavior with faults, and system recovery. The dataset comprises 225 .mat files covering 24 fault cases and one healthy case, each tested under three torque levels and three rotational speeds. The selected operating conditions reflect typical points of a 15-megawatt offshore wind turbine. In addition to the raw data, the dataset includes a Python interface to facilitate visualization. The dataset can support diverse applications, such as validating analytical models of PMSGs, benchmarking fault detection algorithms, and generating synthetic data for further testing. It may also serve as a practical tool in electrical engineering education, especially in courses focused on wind energy systems and fault analysis.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112040"},"PeriodicalIF":1.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145185021","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":"A knowledge graph dataset for broiler farming automatically constructed based on a large language model","authors":"Nan Ma ,&nbsp;Fantao Kong ,&nbsp;Jifang Liu ,&nbsp;Chenyang Zhang ,&nbsp;Chenxv Zhao ,&nbsp;Shanshan Cao ,&nbsp;Wei Sun","doi":"10.1016/j.dib.2025.112018","DOIUrl":"10.1016/j.dib.2025.112018","url":null,"abstract":"<div><div>With the rapid advancement of artificial intelligence, intelligent farming has become a key trend in modern agriculture. In particular, the application of intelligent systems in broiler farming is essential for enhancing production efficiency and optimizing management practices. Broiler farming is a complex process involving multiple interrelated components. However, existing knowledge graphs primarily focus on disease and prevention, making it difficult to capture the intricate interdependencies within the farming process. This limits the effectiveness of knowledge-based support in decision-making. To develop a high-quality broiler farming knowledge system, this study adopts large language modeling technology to integrate a Chinese corpus and construct a comprehensive knowledge graph dataset covering four core dimensions: broiler breeds, farming environment, feeding management, and disease prevention.</div><div>The construction of the dataset involved three key stages. First, text scanning was used to extract information from farming-related literature, while web crawlers collected data from authoritative online sources. The data were then cleaned and manually validated to ensure accuracy and consistency. Second, the DeepKE knowledge extraction framework is used to automatically extract triples related to broiler farming from the text. These are then used as prompts to guide large-scale pre-trained language models (LLMs) to complete and optimize the knowledge, ultimately constructing a relatively complete knowledge graph of broiler farming. Finally, the structured knowledge was stored in a Neo4j graph database to support efficient querying and reasoning.</div><div>The dataset not only provides researchers and farms with multidimensional knowledge of the broiler farming domain, but also supports visual management and analysis, enables data-driven inference through large models, and offers new approaches to optimize farming strategies and enhance production efficiency.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112018"},"PeriodicalIF":1.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095154","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":"92-year meteorological datasets from 50 meteorological stations in Sinaloa, Mexico with daily, monthly, and yearly resolutions.","authors":"Zuriel Dathan Mora-Félix, Sergio Alberto Monjardin-Armenta, Jesús Gabriel Rangel-Peraza, Sergio Arturo Rentería-Guevara, Antonio Jesús Sanhouse-García, Yaneth Bustos-Terrones","doi":"10.1016/j.dib.2025.112041","DOIUrl":"10.1016/j.dib.2025.112041","url":null,"abstract":"<p><p>This paper presents a high-resolution long-term meteorological dataset processed from 50 meteorological stations located in Sinaloa, Mexico. These stations are operated by the Comisión Nacional del Agua (CONAGUA), Servicio Meteorológico Nacional (SMN), and Universidad Autónoma de Sinaloa (UAS). A mean of 21,200 records per meteorological station were processed and prepared over the 1933 - 2025 period. The data is available with daily, monthly, and yearly resolutions. These long-term meteorological datasets can be used for water balances, flood and drought simulations, and identifying climate changes and meteorological anthropogenic influences in the study.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112041"},"PeriodicalIF":1.4,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184953","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":"A synthesized dataset of Pacific Arctic marine mammal occurrences (1860-2024).","authors":"Sydney Waloven, Natalia Portales, Kelly Kapsar, Jianguo Liu","doi":"10.1016/j.dib.2025.112036","DOIUrl":"10.1016/j.dib.2025.112036","url":null,"abstract":"<p><p>Data on marine mammals in the Pacific Arctic region are limited, variably collected over time and by multiple sources. Therefore, collation and synthesis of previously collected data, such as the dataset presented here, make these data more useful to researchers, managers, policy makers, and residents. This paper presents a synthesized dataset of marine mammal occurrences in the Pacific Arctic from 1860 to 2024. Specifically, the dataset focuses on occurrence records of three species - Pacific walrus (<i>Odobenus rosmarus</i>), spotted seal (<i>Phoca largha</i>), and bearded seal (<i>Erignathus barbatus</i>). These species were chosen because of their ecological importance to the Pacific Arctic ecosystem as well as their cultural and subsistence importance to local Indigenous communities. Data were aggregated from open-access, public online data repositories. Datasets were tidied into a standardized format and then integrated to create a singular dataset of occurrence data for these three species. The final dataset contains 36,438 presence-only occurrence points over 164 years throughout the Bering, Chukchi, and Beaufort Seas. This dataset provides stakeholders with quantitative data that can be used to evaluate spatial patterns of marine mammal species over time in the Pacific Arctic region, which can generate insights into the effects of human disturbances (e.g., shipping, resource extraction) and climate change when combined with other environmental variables.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112036"},"PeriodicalIF":1.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184998","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":"Incisor pulp chamber tomographic image dataset for dental age estimation.","authors":"Davi Magalhães Pereira, Alexandre Vieira Pereira Pacelli, William José Lopes Junior, Saulo Moraes Villela, Heder Soares Bernardino, Marcelo Bernardes Vieira, Karina Lopes Devito","doi":"10.1016/j.dib.2025.112033","DOIUrl":"10.1016/j.dib.2025.112033","url":null,"abstract":"<p><p>Accurate tooth detection, segmentation, and age estimation are critical tasks in forensic odontology, helping to identify undocumented individuals in immigration cases, unidentified persons, and in the analysis of corpses and body fragments in accidents. This dataset was assembled to support such applications, comprising images of the upper central incisors from a diverse cohort of Brazilian individuals aged 18 to 60 from the Zona da Mata Mineira, obtained using standardized Cone Beam Computed Tomography (CBCT) scanning protocols. Each scan includes both coronal and sagittal views, providing a comprehensive coverage of the tooth structure. The dataset includes bounding box annotations for precise tooth localization. Additionally, the tooth crown and pulp chamber are essential for age estimation, as the crown's enamel protects the dentin, and the dimensions of the pulp chamber decrease over time due to secondary dentin deposition. Therefore, segmentation annotations are provided for both the crown and pulp chamber to capture these critical structural details. Each image is also labeled with detailed metadata, including age, sex, and tooth number, to support a wide range of research approaches and objectives, maximizing the dataset's utility and making sure it provides comprehensive information for various analytical needs. In addition, the patient code is present in the images, serving as an identifier. This dataset is a valuable resource for researchers aiming to develop and validate machine learning models for various dental applications. Its versatility supports a wide range of tasks, from automated dental diagnostics to advanced forensic investigations, making it a significant contribution to the field of dental image analysis.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"112033"},"PeriodicalIF":1.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12495079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145231759","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":"MeatScan: An image dataset for machine learning-based classification of fresh and spoiled cow meat","authors":"Rose-Mary Owusuaa Mensah Gyening ,&nbsp;Michael Appiah Akoto ,&nbsp;Kwabena Owusu-Agyemang ,&nbsp;Linda Amoako-Banning ,&nbsp;Kate Takyi ,&nbsp;Peter Appiahene","doi":"10.1016/j.dib.2025.112045","DOIUrl":"10.1016/j.dib.2025.112045","url":null,"abstract":"<div><div>This article presents MeatScan<strong>,</strong> a curated image dataset developed to support deep learning-based binary classification of cow meat as fresh or spoiled. The dataset comprises 11,000 high-resolution RGB images (5627 fresh and 5373 spoiled) captured in real-world Ghanaian environments, including open-air markets, butcher shops, and cold storage facilities. Images were labeled based on observable visual cues such as texture, colour, and surface condition, with annotations verified under natural lighting by trained data collectors. MeatScan provides structured and contextually rich visual data for supervised learning in food quality monitoring. It addresses a key gap between advances in computer vision and practical food safety inspection, especially in low-resource settings. The dataset supports experimentation with convolutional neural networks, transfer learning, and data augmentation, and serves as a real-world benchmark for evaluating model robustness to lighting variability, diverse meat textures, and complex backgrounds.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112045"},"PeriodicalIF":1.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044276","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}