Data in Brief最新文献

{"title":"The chicken gut resistome data from different regions of Kazakhstan","authors":"Sergey Shilov ,&nbsp;Ilya Korotetskiy ,&nbsp;Tatyana Kuznetsova ,&nbsp;Natalya Zubenko ,&nbsp;Lyudmila Ivanova ,&nbsp;Elena Solodova ,&nbsp;Alfiya Tugeyeva ,&nbsp;Anzor Kaziyev ,&nbsp;Nadezhda Korotetskaya ,&nbsp;Timur Izmailov","doi":"10.1016/j.dib.2025.111608","DOIUrl":"10.1016/j.dib.2025.111608","url":null,"abstract":"<div><div>Antibiotic resistance (AR) is a serious global health problem affecting both human medicine and animal agriculture. The poultry farming, especially industrial poultry, antibiotics are widely used for disease prevention and growth promotion, leading to the accumulation and dissemination of antibiotic resistance genes (ARGs) within the intestinal microbiomes of birds. Poultry, which often have close contact with humans, can serve as reservoirs for resistant microorganisms, posing potential public health risks. Determination of avian intestinal resistomes through metagenomic sequencing and bioinformatics analysis enables the identification of diversity and transmission dynamics of ARGs, and to evaluate the influence of environmental factors and conditions of poultry on resistance gene distribution.</div><div>The article presents data of resistome analysis of gut microbiota in populations of chickens from different regions of Kazakhstan. The data obtained will allow to develop a strategy to reduce the spread of antibiotic-resistant pathogens and improve safety in poultry farming, as well as to predict the risk of transmission of resistant microorganisms between animals and humans.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111608"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941535","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":"16S rRNA sequencing data of fecal microbiota from juvenile female rats following prenatal poly I:C exposure","authors":"Lirong Yang ,&nbsp;Huiyu Chen ,&nbsp;Menglu Zeng ,&nbsp;Yanfang Lu ,&nbsp;Chen Xu ,&nbsp;Zhenju Cao ,&nbsp;Fuchun Zhong ,&nbsp;Xinyu Yang ,&nbsp;Anying Shen ,&nbsp;Fei Xue ,&nbsp;Wei Lin ,&nbsp;Hua Cao ,&nbsp;Chao Deng ,&nbsp;Yueqing Su","doi":"10.1016/j.dib.2025.111588","DOIUrl":"10.1016/j.dib.2025.111588","url":null,"abstract":"<div><div>With the expanding insights into the “gut-brain axis,” the association between neurodevelopmental disorders (NDDs) and gut microbiota has gained extensive attention in scientific research. Maternal immune activation (MIA) in pregnant females is a crucial environmental risk factor for NDDs in offspring. Polyriboinosinic-polyribocytidylic acid (Poly I:C) belongs to a class of synthetic analogs of double-stranded RNA used to induce MIA in rodents and is widely used in scientific research. The dataset presents 16S rRNA sequencing data of fecal microbiota from juvenile female rats following prenatal Poly I:C exposure. It will help to clarify the impact of prenatal Poly I:C exposure on the intestinal microbiota characteristics of juvenile female offspring rats, reveal the role of the intestinal microbiota in the pathophysiological changes related to maternal immune activation and provide new insights into the mechanism of the microbiota-gut-brain axis in neurodevelopmental disorders.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111588"},"PeriodicalIF":1.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070216","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":"Comprehensive smartphone image dataset for Aegle Marmelos, Hog plum, and lemon plant leaf disease and freshness assessment","authors":"Mohammad Rezwanul Huq,&nbsp;Jubaer Ahmed,&nbsp;Raiyan Gani,&nbsp;Maherun Nessa Isty,&nbsp;Tasmia Islam","doi":"10.1016/j.dib.2025.111590","DOIUrl":"10.1016/j.dib.2025.111590","url":null,"abstract":"<div><div>Fruits, which are packed with nutrients, vitamins, and antioxidants, have been known for their numerous health benefits and curative powers, and are utilized in conventional medicine. Aegle Marmelos, Lemon, and Hog Plum are tangy fruits widely recognized in Asian countries for containing a plentiful supply of bioactive substances. They are also highly valuable in boosting metabolism, possessing tremendous therapeutic properties, and holding financial significance. The leaves of these fruit trees are as essential as their fruits, as they contain versatile medicinal and dietary benefits of immense value. However, these leaves are often affected by various fungal and other diseases, which reduce the ability for healthy growth and productivity of both fruits and leaves. Plants infected with various leaf diseases can produce fewer fruits, which are also of lower quality due to failure to reach maturity and lack of sufficient nutritional value. For these reasons, there is a risk of an outbreak in orchards, which can lead to significant financial losses for both producers and the agricultural sector. This signifies that the early identification of leaf diseases and the management of orchards are essential to minimize the impact of leaf diseases and mitigate these issues, ensuring the healthy production of valuable medicinal fruits. In this paper, various infected leaf images are collected from different regions of Rangpur, providing a comprehensive dataset comprising 3941 images. The dataset includes images of three different plant leaves, where 1513 images of Aegle Marmelos, 1232 images of Lemon, and 1196 of Hog plum, where each of the categories encompasses several classes of common leaf diseases. Through this dataset, an early and accurate digital detection system can be employed, allowing producers to clearly identify diseases instead of relying on traditional methods. The precise and timely identification of leaf diseases enables the control of these diseases by taking necessary actions, ensuring the sustainability of plants, and promoting the healthy growth of these invaluable medicinal fruits.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111590"},"PeriodicalIF":1.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927354","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":"Whole genome sequence data of Bacillus anthracis strain 3B1 isolated from rice soil","authors":"Rosamond Chan ,&nbsp;Kah-Ooi Chua ,&nbsp;Kelly Wan-Ee Teo ,&nbsp;Dedat Prismantoro ,&nbsp;Nurul Shamsinah Mohd Suhaimi ,&nbsp;Abdullah Bilal Ozturk ,&nbsp;Nia Rossiana ,&nbsp;Febri Doni","doi":"10.1016/j.dib.2025.111586","DOIUrl":"10.1016/j.dib.2025.111586","url":null,"abstract":"<div><div>Strain 3B1 was isolated from the soil of rice field cultivated under the system of rice intensification (SRI) in Sukabumi, West Java, Indonesia. The genome of strain 3B1 was sequenced using the MGI DNBSEQ platform, followed by bioinformatics processing, including genome assembly and gene annotation using SPAdes and Prokka, respectively. The assembled genome had a total length of 5,137,985 bp, distributed across 70 contigs, with 5,364 genes identified. Strain 3B1 shared the highest 16S rRNA gene sequence identity including <em>Bacillus paranthracis, B. nitratireducens, B. cereus, B. paramycoides, B. tropicus</em>, and <em>B. anthracis</em>, in the range of 99.86 to 99.93%. Both 16S rRNA gene and core genes-based phylogenetic analyses placed strain 3B1 in the same clade with <em>B. anthracis</em> strain Ames within the <em>Bacillus</em> genus. The phylogenetic placement was supported by the highest average nucleotide identity (ANI) value of 98.1% and digital DNA-DNA hybridization (dDDH) value of 82.7% shared between the genomes of <em>B. anthracis</em> strain Ames and strain 3B1, indicating that 3B1 is a strain of <em>B. anthracis</em>. Further gene annotation revealed that the genome of strain 3B1 lacked the genes encoding for virulence factors such as the <em>pag, cya</em>, and <em>lef</em>. Nonetheless, this data provides valuable insights into the genomic feature of strain 3B1, which can be bioprospected for various biotechnological applications.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111586"},"PeriodicalIF":1.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922237","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":"Mexican dataset of digital mammograms (MEXBreast) with suspicious clusters of microcalcifications","authors":"Ricardo Salvador Luna Lozoya ,&nbsp;Karina Núnez Barragán ,&nbsp;Humberto de Jesús Ochoa Domínguez ,&nbsp;Juan Humberto Sossa Azuela ,&nbsp;Vianey Guadalupe Cruz Sánchez ,&nbsp;Osslan Osiris Vergara Villegas","doi":"10.1016/j.dib.2025.111587","DOIUrl":"10.1016/j.dib.2025.111587","url":null,"abstract":"<div><div>Breast cancer is one of the most prevalent cancers affecting women worldwide. Early detection and treatment are crucial in significantly reducing mortality rates Microcalcifications (MCs) are of particular importance among the various breast lesions. These tiny calcium deposits within breast tissue are present in approximately 30% of malignant tumors and can serve as critical indirect indicators of early-stage breast cancer. Three or more MCs within an area of 1 cm² are considered a Microcalcification Cluster (MCC) and assigned a BI-RADS category 4, indicating a suspicion of malignancy. Mammography is the most used technique for breast cancer detection. Approximately one in two mammograms showing MCCs is confirmed as cancerous through biopsy. MCCs are challenging to detect, even for experienced radiologists, underscoring the need for computer-aided detection tools such as Convolutional Neural Networks (CNNs). CNNs require large amounts of domain-specific data with consistent resolutions for effective training. However, most publicly available mammogram datasets either lack resolution information or are compiled from heterogeneous sources. Additionally, MCCs are often either unlabeled or sparsely represented in these datasets, limiting their utility for training CNNs. In this dataset, we present the MEXBreast, an annotated MCCs Mexican digital mammogram database, containing images from resolutions of 50, 70, and 100 microns. MEXBreast aims to support the training, validation, and testing of deep learning CNNs.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111587"},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927364","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":"Cauliflower leaf diseases: A computer vision dataset for smart agriculture","authors":"Sabbir Hossain Durjoy,&nbsp;Md. Emon Shikder,&nbsp;Md Mehedi Hasan Shoib,&nbsp;Md Hasan Imam Bijoy","doi":"10.1016/j.dib.2025.111594","DOIUrl":"10.1016/j.dib.2025.111594","url":null,"abstract":"<div><div>Cauliflower is among the more well-known vegetables there are. Consumed all around the globe due to it being rich in nutrients such as vitamins, antioxidants, and for being high in fibre. These are nutritional qualities that help with digestion, immune-system, and minimizing inflammation. It is a common issue among farmers to have to deal with various diseases in cauliflower leaves that are difficult to diagnose in their early stages. These diseases have a tendency to propagate in a really swift pace throughout entire fields worth of crops. This in-turn causes heavy losses in the harvest, and makes it much more tedious and resource-intensive to protect the crops. As a result, farmers get more likely to use high amounts of pesticides and harmful chemicals to streamline the process of getting a more reliable yield on their crops. This is not only costly, but it is also harmful both to the quality of crops and to the well-being of the environment. In this publication, we are introducing a dataset containing a considerable number of images of cauliflower leaves. This is intended to drive development on this topic at a faster pace than it is now, and to help enhance disease monitoring, diagnosis, and precautionary techniques. We collected our dataset images between November 2024 and January 2025. In this dataset, cauliflower leaves were categorized into three classes: Healthy, Insect Holes, and Black Rot, each reflecting a specific condition that impacts plant health at different stages. This dataset consists of 2,661 images. The pictures were captured at different locations in Bangladesh, under different weather conditions, dates, temperatures, and with different devices. To enhance the data quality, we used several steps to process the dataset, making sure it would reflect real-world conditions and be ready for training. The images were resized to a standard size of 3000 × 3000 pixels, brightness was adjusted to make the images more easily discernible, and we removed duplicates and poor-quality images. These actions helped ensure the dataset was in the best possible shape for effective model training. This dataset will be highly effective for agricultural research, precision agriculture, and effective management of diseases. It should help develop highly accurate machine learning models for early detection of Cauliflower leaf diseases. The dataset is employed to train deep learning models to support automated monitoring and smart decision-making in precision agriculture. This data set also has immense potential for real-time and practical use. It can be utilized to develop applications like mobile apps or automated systems where farmers can easily identify diseases at early stages and take immediate action, without the requirement of expert on-site knowledge. This data set can also be utilized with smart farming equipment like drones and sensors to track big fields in real time.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111594"},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922239","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":"Improving internet of vehicles research: A systematic preprocessing framework for the VeReMi dataset","authors":"Aparup Roy ,&nbsp;Debotosh Bhattacharjee ,&nbsp;Ondrej Krejcar","doi":"10.1016/j.dib.2025.111599","DOIUrl":"10.1016/j.dib.2025.111599","url":null,"abstract":"<div><div>The Vehicular Reference Misbehavior Dataset (VeReMi) is a vital resource for advancing Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV). However, its large size (∼7 GB) and inherent class imbalance pose significant challenges for machine learning model development. This paper presents a preprocessing framework to enhance VeReMi’s usability and relevance. Through 10 % down-sampling, the dataset was reduced to ∼724MB, making it computationally manageable. Biases were addressed by balancing benign and malicious samples through synthesis and identifying benign instances using predefined criteria. A refined feature set, including key attributes like <em>rcvTime, pos_0, pos_1,</em> and <em>attack_type</em> (renamed <em>attacker_type</em>), was selected to improve machine learning compatibility. This preprocessing pipeline effectively maintains data integrity and preserves the representativeness of malicious patterns. The optimized dataset is well-suited for ITS and IoV applications, such as anomaly detection and network security, underscoring the crucial role of preprocessing in overcoming real-world constraints and enhancing model performance.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111599"},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927365","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":"MUTFER2024: A new dataset for South African emotion recognition","authors":"Rogerant Tshibangu ,&nbsp;Jules R Tapamo","doi":"10.1016/j.dib.2025.111592","DOIUrl":"10.1016/j.dib.2025.111592","url":null,"abstract":"<div><div>Facial Emotion Recognition (FER) plays a critical role in applications such as human-computer interaction, security, and healthcare. The effectiveness of FER systems largely depends on the quality and diversity of the datasets used for training and evaluation. However, existing FER datasets often lack adequate representation of African populations, leading to racial biases in recognizing emotions across diverse ethnic groups. This issue arises from the predominance of Western-centric datasets used in training FER systems, which results in inaccurate and biased outcomes when applied to African or non-Caucasian faces.</div><div>To address this limitation, we introduce MUTFER2024, a novel dataset developed at Mangosuthu University of Technology. MUTFER2024 aims to minimize racial bias in FER systems by providing an extensive collection of facial emotion images from African participants. The dataset comprises 13,032 images collected from 300 individuals, including students and staff members, and is categorized into seven emotion classes: happy, sad, angry, surprised, neutral, disgusted, and fearful.</div><div>This paper details the methodology employed in data collection, segmentation, and categorization. Facial emotion images were gathered through structured submission protocols to ensure diversity in expressions. Subsequently, the images were meticulously segmented and categorized into the specified emotion classes. Data were collected under real-world conditions using mobile and computer cameras. The dataset is hosted on GitHub and can be used to train emotion recognition models for underrepresented African populations.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111592"},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927367","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":"First spectral reflectance dataset of a highly invasive and aggressive alien plant species, Equisetum hyemale (snakegrass), in South Africa: Towards early detection and effective eradication","authors":"Lesibana Sedibana ,&nbsp;Mahlatse Kganyago ,&nbsp;Thulisile Jaca ,&nbsp;Kowiyou Yessoufou","doi":"10.1016/j.dib.2025.111582","DOIUrl":"10.1016/j.dib.2025.111582","url":null,"abstract":"<div><div>This paper presents the first spectral reflectance dataset of snakegrass (Equisetum hyemale L.) invasive alien species recorded in South Africa. A total of 338 plant specimens were collected in Howick, KwaZulu-Natal province, and carefully stored in a cooler box for less than 24 hours of collection to retain the structural and biochemical state of the specimens and their overall characteristics. Then, spectral reflectance measurements were collected under laboratory conditions using the PSR-300 Spectral Evolution full-range spectrometer, equipped with a bifurbucated cable, a leaf clip and an artificial lighting system. Next, spectral preprocessing was performed in R statistical software to remove noisy spectra and regions and perform averaging per sample. The dataset is critical for early detection of the species and spatial distribution mapping using remotely piloted systems and earth observation satellites, providing essential information for aiding containment and eradication efforts.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111582"},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922240","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":"3DZSignDB: 3D avatar SigML data for Algerian sign language","authors":"Taha Zerrouki ,&nbsp;Mohamed Fares Slimani ,&nbsp;Amine Mami ,&nbsp;Redha Mazari","doi":"10.1016/j.dib.2025.111568","DOIUrl":"10.1016/j.dib.2025.111568","url":null,"abstract":"<div><div>We present a dataset of Algerian Sign Language (ALSL) used to develop a translation system into a 3D avatar. We used the Notation System Method, which is mostly based on the Hamburg Notation System (HamNoSys), to encode 417 ALSL lexical signs. The data collection process involved collaboration with ALSL translation experts to ensure the consistency of sign representations.</div><div>NSM transcribed each sign, capturing essential linguistic features such as hand shape, movement, and facial expressions. The resulting dataset was used for a real-time translation system that converts written Arabic words into sign language represented by a 3D avatar.</div><div>Applications for the dataset include sign language education and assistive technologies for the deaf community. You can use it to improve systems for automatic sign language translation. The structured and annotated ALSL lexicon that is provided helps with the ongoing development of technologies that allow everyone to communicate and with making more progress in recognizing and synthesizing sign language.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111568"},"PeriodicalIF":1.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902252","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}