Data in Brief最新文献

{"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}

{"title":"Software bug report dataset from Eclipse projects","authors":"Noelia Lopez-Duran,&nbsp;David Romero-Organvidez,&nbsp;Fermín L. Cruz,&nbsp;David Benavides","doi":"10.1016/j.dib.2025.112016","DOIUrl":"10.1016/j.dib.2025.112016","url":null,"abstract":"<div><div>In recent decades, the analysis of data from software projects — including source control systems, defect tracking systems, and code review repositories — has greatly improved our understanding of software development and its evolution. However, obtaining this information can be time-consuming, and the extracted data is not always well-maintained. This paper introduces an extensive dataset generated from Bugzilla repositories, focusing on key products from the Eclipse bug-tracking system. This dataset addresses the need for up-to-date data in existing repositories, preserving crucial historical information that may be lost due to the transition from Bugzilla to newer bug-tracking systems like Jira or GitHub Issues. Our dataset includes 301,378 bug reports along with all related information, organised into different folders that indicate the project in which the bug was filed. Additionally, we present a custom and lightweight Command Line Interface (CLI) tool designed to efficiently extract detailed information from Bugzilla repositories, automating data collection across various Bugzilla instances. The dataset and tool can be utilized for defect prediction, software maintenance, and evolutionary analysis. To the best of our knowledge, this is the largest, most complete, and up-to-date dataset of Eclipse bug reports available.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112016"},"PeriodicalIF":1.4,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044273","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":"LLM-based assessment of HTTPS cybersecurity awareness: Dataset from moroccan web users and webmasters","authors":"Abdelhadi Zineddine ,&nbsp;Abdeslam Rehaimi ,&nbsp;Mohamed Zaoui ,&nbsp;Yousra Belfaik ,&nbsp;Yassine Sadqi ,&nbsp;Said Safi","doi":"10.1016/j.dib.2025.112024","DOIUrl":"10.1016/j.dib.2025.112024","url":null,"abstract":"<div><div>Cybersecurity awareness plays a fundamental role in protecting digital communications, particularly in the deployment and use of the HTTPS protocol. While previous studies have explored website security practices, there is a lack of available datasets that empirically assess both awareness levels and implementation behaviors of web-users and website administrators. This dataset addresses this gap by analyzing cybersecurity awareness and HTTPS-related behaviors of 440 Moroccan voluntary participants, including web users and webmasters. Data was collected via a structured Google Forms survey, disseminated through web development and cybersecurity communities on online platforms such as Facebook, WhatsApp and LinkedIn.</div><div>The responses collected from multiple-choice questions (MCQs) and free-text entries (categorized using the GPT-4o large language model (LLM)) were pre-processed and score-encoded according to a predefined mapping scheme. Participants’ awareness levels were classified as Low, Moderate, or High on total scores. To identify behavioral patterns, the unsupervised KMeans clustering algorithm was applied separately to user and webmaster groups. Principal Component Analysis (PCA) and LLM-based interpretation provided insights into awareness profiles and cybersecurity risk behaviors.</div><div>The dataset includes raw survey responses, score-encoded data, clustering outputs, and LLM-generated awareness assessment reports. It serves both as supplementary material for a novel hybrid cybersecurity assessment methodology for HTTPS deployment presented in [1], and as a standalone resource for researchers and practitioners examining HTTPS usage, certificate management, and behavioral risk profiling. This dataset is a valuable asset for empirical research and practical improvements in cybersecurity awareness within role-based and regional web ecosystems.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112024"},"PeriodicalIF":1.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044275","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":"JujubeBruiseNet: A high-resolution image dataset for bruise detection in Ziziphus mauritiana","authors":"Md Arham Tabib,&nbsp;Sumyia Sabrin Liza,&nbsp;Md Mizanur Rahman","doi":"10.1016/j.dib.2025.112031","DOIUrl":"10.1016/j.dib.2025.112031","url":null,"abstract":"<div><div>The article presents JujubeBruiseNet, a high-resolution image dataset designed for bruise detection in <em>Ziziphus mauritiana</em> (jujube) fruits. <em>Ziziphus mauritiana</em> is a seasonal fruit often found in late summer to early fall. The bruise detection in this fruit is crucial for post-harvesting, fruit processing, and food packaging. Manual detection of bruises is time-consuming and often leads to inaccuracy. Therefore, developing a novel classification model is essential, which will immediately recognize bruises in the fruits and, as a result, decrease human effort, expenses, and production time in the agriculture sector. The dataset contains a total of 1464 original photos categorized by two classes labelled Healthy and Bruised. We collected the fruit from the local market and fields near Savar, Dhaka, Bangladesh, with the help of domain experts in the period from 10th March to 20th March 2025. To reduce outside variations and provide uniformity, the photos were taken under precisely controlled lighting. This article offers a major dataset for researchers to develop effective quality assessment models for post-harvesting fruit sorting and classification. Convolutional neural networks (CNNs) and other computer vision models can be trained exclusively using this dataset to increase the precision of agricultural product bruise recognition. The dataset can facilitate research in computer vision-based agricultural monitoring and fruit quality evaluation, openly accessible on Mendeley Data, link: JujubeBruiseNet: A Dataset for Bruise Detection in Ziziphus mauritiana - Mendeley Data</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112031"},"PeriodicalIF":1.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044274","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":"Validated dataset combining simulations and measurements for emission analysis of naturally ventilated dairy barns","authors":"Julian Hartje,&nbsp;Abu Zar Shafiullah","doi":"10.1016/j.dib.2025.112017","DOIUrl":"10.1016/j.dib.2025.112017","url":null,"abstract":"<div><div>Quantifying emissions from naturally ventilated livestock buildings is challenging due to the large side wall openings. In addition, measurement campaigns are expensive and time consuming and are therefore limited to a few short measurement weeks during the year. However, emission factors or annual averages are extrapolated from these data sets. Simulations can complement this data set by extending it and thus broadening the basis for the extrapolation of emission factors or evaluation of the barn and management system. The dataset presented consists of solution data from computational fluid dynamics (CFD) simulations of naturally ventilated cattle barns and the corresponding simulation and geometry files. The simulations were validated using data sets from measurement campaigns in three naturally ventilated cattle barns in Germany. Together with weather data from the German Weather Service (DWD), weather situations that occurred outside the measurement weeks could be investigated. With the presented data set further investigations are possible. Together with the measured data, simulation techniques, data aggregation and the development of new numerical modelling approaches can be investigated in detail.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112017"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019056","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 real-world iiot dataset for predictive maintenance of metalworking fluids","authors":"Carlos Cambra,&nbsp;Félix Movilla,&nbsp;Félix de Miguel,&nbsp;Daniel Urda,&nbsp;Nuria Velasco,&nbsp;Álvaro Herrero","doi":"10.1016/j.dib.2025.112020","DOIUrl":"10.1016/j.dib.2025.112020","url":null,"abstract":"<div><div>This article presents a multivariate time series dataset detailing the physicochemical degradation of an industrial metalworking fluid (MWF). The data were collected continuously over several months from a test tank under typical operational conditions at an industrial facility in Spain. Four critical variables were monitored using industrial-grade sensors: pH, temperature, concentration, and conductivity. The dataset is provided in five CSV files. The primary file, measures.csv, contains the preprocessed time series at a uniform 5-minute frequency, with authentic missing data gaps intentionally preserved to reflect real-world sensor and connectivity issues. The four additional files serve as a comprehensive benchmark for data imputation algorithms. Each of these benchmark files corresponds to a single variable and includes the original data alongside imputed values generated by five distinct methods: K-Nearest Neighbours (KNN), a hybrid model (HybridKCL), an LSTM-based Variational Autoencoder (LSTM-VAE), and both pre-trained and fine-tuned versions of the MOMENT foundation model. This resource enables researchers and practitioners to develop, validate, and compare predictive maintenance models, anomaly detection systems, and advanced imputation techniques. Furthermore, it serves as a valuable educational tool for addressing common challenges in industrial IoT data, fostering advancements in sustainable and efficient manufacturing.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112020"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019055","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":"FTIR spectroscopy and VSC-based colour assessment dataset for comparative analysis of cremated bones","authors":"Anu Lillak ,&nbsp;Tim Thompson ,&nbsp;Mari Tõrv ,&nbsp;Ester Oras","doi":"10.1016/j.dib.2025.112019","DOIUrl":"10.1016/j.dib.2025.112019","url":null,"abstract":"<div><div>The data presented in this article derives from archaeological cremated bones excavated in 2014–2015 at Aakre Kivivare tarand cemetery, S Estonia. The material covers bone fragments of different colours to be assessed visually, using Video Spectral Comparator (VSC) and analysed comparatively with Fourier Transform Infrared Spectroscopy (FTIR) to determine the structural and compositional changes in the thermally altered bone and implications of the latter in bone colouring.</div><div>The dataset comprises FTIR spectra measurements, colour spectra measured with VSC and visually assessed colour of human and animal bones chosen for the study. This dataset is expected to be a comparative source for determining archaeological cremated bone colour induced by heat-related changes in the bone microstructure, supporting the visual estimations of temperature-based cremation practices in archaeological and forensic bone material in the future.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112019"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003778","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":"KurdABSA: Kurdish aspect-based sentiment analysis dataset curation using few-shot learning","authors":"Rania Azad M. San Ahmed,&nbsp;Soran AB. Saeed","doi":"10.1016/j.dib.2025.112012","DOIUrl":"10.1016/j.dib.2025.112012","url":null,"abstract":"<div><div>Aspect-Based Sentiment Analysis (ABSA) extends traditional sentiment analysis by not only identifying the overall sentiment of a text but also associating specific sentiments with deeper and granular insights. The main objective of ABSA is to accurately extract relevant aspects and determine the sentiment polarity associated with each. Although extensive research has been conducted on ABSA across various languages, low-resource languages such as Kurdish remain largely underexplored in this domain. To address this gap, the present study introduces the first publicly available aspect-based sentiment analysis dataset for the Sorani dialect of Kurdish, addressing a critical gap in natural language processing (NLP) research for low-resource languages. The dataset has &gt;4000 quadruplet ABSA in the restaurant review domain, written in the Kurdish language (Sorani dialect) using the Perso-Arabic script. A prompt-based few-shot learning model was employed to automatically annotate the dataset with aspect-opinion-category-sentiment quadruples, guided by a manually annotated support set verified by native Kurdish-language experts. This resource is intended for use in machine learning, deep learning, and cross-lingual model adaptation, making it suitable for training, fine-tuning, and benchmarking.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112012"},"PeriodicalIF":1.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019122","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 long-time dataset of climate related events on Cyprus Island (1174–1913)","authors":"Emmanuel Eliot ,&nbsp;Gilles Grivaud ,&nbsp;Victor Beauvalet ,&nbsp;Raphaëlle Krummeich ,&nbsp;Armelle Couillet ,&nbsp;Iris Charalambidou ,&nbsp;Romain Reulier ,&nbsp;Salih Gücel ,&nbsp;Sebastien Rey-Coreyhourcq ,&nbsp;Kyriakos Georgiou ,&nbsp;Carole Nehme","doi":"10.1016/j.dib.2025.112007","DOIUrl":"10.1016/j.dib.2025.112007","url":null,"abstract":"<div><div>The dataset aims to illustrate societal vulnerability to climatic conditions on the island of Cyprus between 1174 and 1913. It describes a dataset of events including epidemic and pest outbreaks, natural and social disasters. This dataset is unique for two reasons. Firstly, it is the most complete collection of events based mainly on secondary sources for the island, consisting of international and local historical sources dedicated to illustrating social vulnerability to climate. Secondly, it is a long time series (1174 to 1913) covering different periods of rule on the island. This study presents research material that contribute to better understand climate changes over time and build up a local long times series in a region where archives are scattered and less compiled in comparison with Western Europe.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112007"},"PeriodicalIF":1.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988537","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":"MDER-MA: A multimodal dataset for emotion recognition in low-resource Moroccan Arabic language","authors":"Soufiyan Ouali,&nbsp;Said El Garouani","doi":"10.1016/j.dib.2025.112005","DOIUrl":"10.1016/j.dib.2025.112005","url":null,"abstract":"<div><div>Emotion recognition and analysis have become increasingly important in recent years, particularly with the rapid digitization and automation of virtual agents. As these systems are integrated into various aspects of daily life, enabling them to understand and respond to human emotions is essential for creating more natural, empathetic, and effective interactions. Humanizing virtual agents through emotion-aware capabilities enhances user experience, plays a critical role in emotion-driven services, such as personalized customer support and mental health assistance, thereby offering significant socio-economic benefits. Despite the significant advancements achieved in high-resource languages within this field, these results cannot be directly generalized to low-resource languages such as Moroccan Arabic. This is because emotional expression is highly influenced by cultural, regional, and linguistic factors, leaving a gap between research on high- and low-resource languages. One of the major challenges hindering the development of such a powered system is the lack of a high-quality and realistic dataset. This paper introduces MDER-MA, a comprehensive multimodal dataset designed for emotion recognition in the Moroccan Arabic dialect. Contains 5288 data items that express one of the four emotions: Happy, Sad, Angry, and Neutral, expressed in four different modalities: audio, text, spectrogram, and Mel-spectrogram images. Each modality contains 1322 samples. The samples were collected from various regions across Morocco to ensure the creation of a representative dataset that is not biased toward any single geographic or linguistic area. MDER-MA supports multiple applications, including emotion recognition, audio transcription, age and gender identification from both speech, text, and image modalities. Annotation was conducted by five native Moroccan speakers, ensuring high linguistic reliability for real-time emotion recognition tasks. This work aims to bridge the gap between high-resource and low-resource languages in the field of emotion-aware and humanized intelligent systems, and to foster the development of Arabic language technologies, with particular attention to regional dialects such as Moroccan Arabic.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"62 ","pages":"Article 112005"},"PeriodicalIF":1.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922443","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}