Comprehensive Dataset Research Articles

Open access publications drive few visits from Google Search results to institutional repositories

AbstractGiven the importance of Google Search in generating visits to institutional repositories (IR), a lack of visibility in search engine results pages can hinder the possibility of their publications being found, read, downloaded, and, eventually, cited. To address this, institutions need to evaluate the visibility of their repositories to determine what actions might be implemented to enhance them. However, measuring the search engine optimization (SEO) visibility of IRs requires a highly accurate, technically feasible method. This study constitutes the first attempt to design such a method, specifically applied here to measuring the IR visibility of Spain’s national university system in Google Search based on a set of SEO-based metrics derived from the Ubersuggest SEO tool. A comprehensive dataset spanning three months and comprising 217,589 bibliographic records and 316,899 organic keywords is used as a baseline. Our findings show that many records deposited in these repositories are not ranked among the top positions in Google Search results, and that the most visible records are mainly academic works (theses and dissertations) written in Spanish in the Humanities and Social Sciences. However, most visits are generated by a small number of records. All in all, our results call into question the role played by IRs in attracting readers via Google Search to the institutions’ scientific heritage and serve to underscore the prevailing emphasis within IRs on preservation as opposed to online dissemination. Potential improvements might be achieved using enhanced metadata schemes and normalized description practices, as well as by adopting other actionable insights that can strengthen the online visibility of IRs. This study increases understanding of the role played by web indicators in assessing the web-based impact of research outputs deposited in IRs, and should be of particular interest for a range of stakeholders, including open access and open science advocates, research agencies, library practitioners, repository developers, and website administrators.

Segmenting the Chinese New Energy Compact SUV Market: A Latent Class Analysis of Consumer Preferences

The burgeoning new energy vehicle (NEV) sector in China, particularly the compact SUV market, presents a dynamic landscape for consumer preferences and behaviors. This study employs a latent class analysis (LCA) to segment the Chinese new energy compact SUV market, aiming to identify distinct consumer groups with homogeneous preferences and behaviors. Utilizing a comprehensive dataset of 6,720 consumer samples sourced from a reliable automotive statistics website, the study delineates two primary consumer segments: "Luxury Brand Preference Buyers" and "Normal Brand Rejection Buyers." The findings reveal that the former segment exhibits a marked preference for luxury brands, and tends to make fewer purchases, predominantly in the last quarter of the year 2023. In contrast, the latter segment shows a strong preference for another luxury brand and a balanced distribution of purchase quantities throughout the year, with a clear rejection of normal brands. These insights are instrumental for NEV enterprises to develop targeted marketing strategies that cater to the specific needs and preferences of these consumer segments, thereby enhancing market share, consumer satisfaction, and loyalty.

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 m6A reader protein

Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitors for a target is limited, data augmentation strategies are often preferred to enhance model performance. In this study, we developed predictive machine learning models for structure-based drug discovery leveraging multiple traditional machine learning algorithms trained with target and ligand dynamics-aware datasets.To illustrate our approach, we present a composite model that combines classification and regression to predict YTHDF1 inhibitors, utilizing PLEC features. YTHDF1, a key m6A reader protein involved in mRNA translation, is implicated in various cancers, making it a promising therapeutic target. Traditional structure-based virtual screening (SBVS) using generic scoring functions has struggled to identify potent YTHDF1 inhibitors due to the protein's unique binding characteristics. To overcome this, we developed YTHDF1-specific machine learning scoring functions (MLSFs) to enhance SBVS efficacy.We employed various data augmentation techniques to generate a comprehensive dataset, incorporating multiple conformations of ligands and the YTHDF1 protein. We have trained 64 YTHDF1-specific MLSFs using four machine learning algorithms and evaluated them on ten test sets, focusing on their predictive and ranking power. Our results demonstrate that the artificial neural network with protein-ligand extended connectivity fingerprints (ANN-PLEC) outperforms other MLSFs, consistently achieving high area under the precision-recall curve (PR-AUC) of 0.87. This method shows promise for targets with limited quantities of active molecules, providing a viable path forward for drug discovery research. The ANN-PLEC scoring function is made freely available on GitHub for other researchers to access and utilize https://github.com/JuniML/SBVS-YTHDF1/.

A community effort to optimize sequence-based deep learning models of gene regulation.

A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural networks but diverged in architectures and training strategies. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide models into modular building blocks. We tested all possible combinations for the top three models, further improving their performance. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets, demonstrating the progress that can be driven by gold-standard genomics datasets.

Corporate hypocrisy and ESG rating divergence

AbstractWe draw upon impression management theory and cognitive dissonance theory to examine whether or not corporate hypocrisy triggers substantial environmental, social, and governance (ESG) rating divergence and to further investigate the influence channels of cognitive dissonance and impression management. In addition, our study explores the moderating role of media coverage with different emotional biases in shaping the relationship between corporate hypocrisy and ESG rating divergence. By utilizing a comprehensive dataset comprising 3562 Chinese listed companies and 25,898 firm‐year observations from 2010 to 2022, findings reveal that hypocritical firms exhibit significantly large ESG rating divergence. In particular, these firms induce cognitive dissonance among rating agencies, resulting in rating disparities, which are further magnified when using impression management through the selective disclosure of negative information or symbolic promotion of positive information. Furthermore, positive media coverage weakens the positive association between corporate hypocrisy and ESG rating divergence, whereas negative media coverage strengthens this relationship. Lastly, we find that the impact of corporate hypocrisy on ESG rating divergence is more pronounced for companies with higher average ESG scores, lower long‐term charitable involvement, and lower information opacity. These findings withstand various sensitivity tests utilizing alternative measures for rating divergence and corporate hypocrisy, as well as different sample compositions. Our conclusions remain robust even after addressing endogeneity concerns using a two‐stage least squares regression model.

Gene regulatory dynamics during the development of a paleopteran insect, the mayfly Cloeon dipterum.

The evolution of insects has been marked by the appearance of key body plan innovations that promoted the outstanding ability of this lineage to adapt to new habitats, boosting the most successful radiation in animals. To understand the evolution of these new structures, it is essential to investigate which genes and gene regulatory networks participate during the embryonic development of insects. Great efforts have been made to fully understand gene expression and gene regulation during the development of holometabolous insects, in particular Drosophila melanogaster. Conversely, functional genomics resources and databases in other insect lineages are scarce. To provide a new platform to study gene regulation in insects, we generated ATAC-seq for the first time during the development of the mayfly Cloeon dipterum, which belongs to Paleoptera, the sister group to all other winged insects. With these comprehensive datasets along six developmental stages, we characterized pronounced changes in accessible chromatin between early and late embryogenesis. The application of ATAC-seq in mayflies provides a fundamental resource to understand the evolution of gene regulation in insects.

Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.

Storm clouds over innovation: Typhoon shocks and corporate R&D activities

This study investigates the economic impact of typhoon disasters on corporate innovation activities, a critical driver of economic growth and resilience. Utilizing a comprehensive dataset of Chinese public companies from 2010 to 2022 and employing a wind field model to construct a city-level typhoon destruction index, we apply difference-in-differences and instrumental variable methods to analyze the effects on corporate innovation. Our findings reveal that typhoon disasters significantly reduce both patent filings and R&D investment intensity, particularly in coastal areas and technology-intensive industries. The adverse effects are transmitted through financial constraints, human capital loss, and infrastructure damage. This research contributes to understanding the microeconomic implications of extreme weather events on innovation-driven economic growth, offering insights for disaster resilience policies in vulnerable regions.

A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

Recent years have witnessed significant advancements in machine perception, particularly in the context of self-driving vehicles. The accurate detection and interpretation of road signs by these vehicles are crucial for enhancing safety, intelligence, and efficiency on the roads. Consequently, there is a growing body of research dedicated to improving traffic sign recognition technologies, a key component of intelligent transportation systems. Annual statistics highlight numerous accidents attributable to factors such as excessive speed, variable lighting conditions, and the misinterpretation of traffic signs. In response to these challenges, a novel approach for the rapid and reliable recognition of traffic signs by moving vehicles has been developed. This approach leverages a custom dataset encompassing twelve object categories and seven subcategories, reflective of road sign diversities encountered in India. A specialized algorithm, TrafficSignNet, was devised to specifically identify signs related to speed, turning, zones, and bumps. This algorithm was trained on a comprehensive dataset comprising 4,962 images, with its performance evaluated using 705 images from real traffic scenarios. The evaluation demonstrates that the model achieves remarkable accuracy across various lighting conditions, processing up to 12 frames per second. This processing rate is compatible with the high-definition standards of contemporary vehicle cameras, which is 1280 × 720 pixels. The model's effectiveness is quantified through accuracy, precision, recall, and F1 score, with respective values of 0.985, 0.978, 0.964, and 0.971, showcasing its potential to significantly contribute to the advancement of smart transportation systems.

A large multi-focus dataset for white blood cell classification

The White Blood Cell (WBC) differential test ranks as the second most frequently performed diagnostic assay. It requires manual confirmation of the peripheral blood smear by experts to identify signs of abnormalities. Automated digital microscopy has emerged as a solution to reduce this labor-intensive process and improve efficiency. Several publicly available datasets provide various WBC subtypes of differing quality and resolution. These datasets have contributed to advancing WBC classification using machine learning techniques. However, digital microscopy of blood cells with high magnification often requires a wider depth of field, posing challenges for automatic digital microscopy that necessitates capturing multiple stacks of focal planes to obtain complete images of specific blood cells. Our dataset provides 25,773 image stacks from 72 patients. The image labels consist of 18 classes encompassing normal and abnormal cells, with two experts reviewing each label. Each image includes 10 z-stacks of cropped 200 by 200 pixel images, captured using a 50X microscope with 400 nm intervals. This study presents a comprehensive multi-focus dataset for WBC classification.

Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R2) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R2 of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices.

AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection.

Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI). By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2. Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models. These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows.

How unemployment benefit duration shapes startup motivation and growth

AbstractBusiness creation is economically important, and unemployment precedes the creation of a substantial share of new firms. Yet, most research has focused on analyzing the effects of unemployment insurance policies on re-employment outcomes, ignoring self-employment. In this paper, we analyze how the potential duration of unemployment benefits, a fundamental design choice of unemployment insurance systems, affects whether new firms are founded out of opportunity or necessity and their growth potential. To this end, we construct a comprehensive dataset on German firm founders that links administrative social insurance information with business survey data. Exploiting reform and age-related exogenous variation in the potential duration of unemployment benefits, we find that longer potential benefit duration implies longer actual unemployment and, as a consequence, more necessity entrepreneurship and worse startup outcomes in terms of sales and employment growth. We explain this overall effect of potential benefit duration through a mix of compositional and individual-level duration effects. Our findings underline that new firms started out of unemployment are a highly heterogeneous group and suggest that the (optimal) design of unemployment insurance systems has important externalities on whether innovation- and growth-oriented firms are started out of unemployment.

Estimation of nighttime PM2.5 concentrations over Seoul using Suomi NPP/VIIRS Day/Night Band

With rapid economic development and urban growth, Seoul experiences severe air pollution due to fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), which is detrimental to human health. Although recent studies have extensively focused on estimating daytime PM2.5 concentrations using various types of satellite data, there remains a significant lack of research on nighttime PM2.5 estimations. This study estimated nighttime PM2.5 in Seoul from December 2018 to November 2019 using multiple linear regression (MLR) and random forest (RF) models. These models, which incorporated data on radiance, moon illumination fraction, and terrain height from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) on board the Suomi National Polar-orbiting Partnership satellite covering all moon phases, also utilized meteorological data from the ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF). To address multicollinearity, seasonal models were developed using forward stepwise regression and variance inflation factor analysis. DNB radiance analysis indicates that the high intensity of artificial light sources in Seoul significantly reduces the impact of moonlight, leading to notable changes in the DNB radiation associated with PM2.5 concentrations. Consequently, this study estimated nighttime PM2.5 over Seoul across all moon phases. These estimates were then validated through 10-fold cross-validation. The RF model exhibited superior accuracy, with a coefficient of determination (R2) of 0.65–0.90, compared to MLR, with R2 of 0.15–0.50, reflecting seasonal fluctuations in the model performance. The developed models can be applied to estimate reliable nighttime PM2.5 concentrations in megacities with strong artificial light sources, utilizing a comprehensive dataset from satellite observations for all moon phases. Additionally, our findings can serve as scientific data for establishing environmental policies by providing valuable insights into understanding air pollution primarily caused by PM2.5.

Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo.

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce "RetChemQA", a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 question and answers (Q&As) for each type. The questions have been extracted from an extensive corpus of literature containing about 2,530 research papers from publishers including NAS, ACS, RSC, Elsevier, and Nature Publishing Group, among others. The dataset has been generated using OpenAI's GPT-4 Turbo, a cutting-edge model known for its exceptional language understanding and generation capabilities. In addition to the Q&A dataset, we also release a dataset of synthesis conditions extracted from the corpus of literature used in this study. The aim of RetChemQA is to provide a robust platform for the development and evaluation of advanced machine learning algorithms, particularly for the reticular chemistry community. The dataset is structured to reflect the complexities and nuances of real-world scientific discourse, thereby enabling nuanced performance assessments across a variety of tasks.

Advancing non-alcoholic fatty liver disease prediction: a comprehensive machine learning approach integrating SHAP interpretability and multi-cohort validation.

Non-alcoholic fatty liver disease (NAFLD) represents a major global health challenge, often undiagnosed because of suboptimal screening tools. Advances in machine learning (ML) offer potential improvements in predictive diagnostics, leveraging complex clinical datasets. We utilized a comprehensive dataset from the Dryad database for model development and training and performed external validation using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020 cycles. Seven distinct ML models were developed and rigorously evaluated. Additionally, we employed the SHapley Additive exPlanations (SHAP) method to enhance the interpretability of the models, allowing for a detailed understanding of how each variable contributes to predictive outcomes. A total of 14,913 participants were eligible for this study. Among the seven constructed models, the light gradient boosting machine achieved the highest performance, with an area under the receiver operating characteristic curve of 0.90 in the internal validation set and 0.81 in the external NHANES validation cohort. In detailed performance metrics, it maintained an accuracy of 87%, a sensitivity of 92.9%, and an F1 score of 0.92. Key predictive variables identified included alanine aminotransferase, gammaglutamyl transpeptidase, triglyceride glucose-waist circumference, metabolic score for insulin resistance, and HbA1c, which are strongly associated with metabolic dysfunctions integral to NAFLD progression. The integration of ML with SHAP interpretability provides a robust predictive tool for NAFLD, enhancing the early identification and potential management of the disease. The model's high accuracy and generalizability across diverse populations highlight its clinical utility, though future enhancements should include longitudinal data and lifestyle factors to refine risk assessments further.

Dental Composite Performance Prediction Using Artificial Intelligence.

There is a need to increase the performance and longevity of dental composites and accelerate the translation of novel composites to the market. This study explores artificial intelligence (AI), specifically machine learning (ML), to predict the performance outcomes (POs) of dental composites from their composite attributes (CAs). An extensive dataset from over 200 publications was built and refined to 233 samples with 17 CAs and 7 POs. Nine ML models were evaluated for PO prediction performance using classified data, and Five ML models were evaluated for PO regression analysis. The KNN model excelled in predicting flexural modulus (FlexMod), Decision Tree model in flexural strength (FlexStr) and volumetric shrinkage (ShrinkV), and Logistic Regression and SVM models in shrinkage stress (ShrinkStr). Receiver operating characteristic area under the curve (ROC AUC) analysis confirmed these results but found that Random Forest was more effective for FlexStr and ShrinkV, suggesting the possibility of Decision Tree overfitting the data. Regression analysis revealed that the Voting Regressor was superior for FlexMod and ShrinkV predictions, while Decision Tree Regression was optimal for FlexStr and ShrinkStr. Feature importance analysis indicated TEGDMA is a key contributor to FlexMod and ShrinkV, BisGMA and UDMA to FlexStr, and depth of cure, degree of monomer-to-polymer conversion, and filler loading to ShrinkStr. There is a need to conduct a full analysis using multiple ML models because different models predict different POs better, and for a large, comprehensive dataset to train robust AI models to facilitate the prediction and optimization of composite properties and support the development of new dental materials.

A Comparative Study of Machine Learning Models for Predicting Customer Churn in Retail Banking: Insights from Logistic Regression, Random Forest, GBM, and SVM

Customer churn poses a significant challenge in the retail banking sector, leading to substantial financial losses and undermining long-term growth. This study explores the effectiveness of various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), in predicting customer churn. Utilizing a comprehensive dataset derived from a leading bank, we conducted extensive data preprocessing and feature engineering before evaluating model performance through metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Our findings reveal that the Gradient Boosting Machine outperforms its counterparts, achieving an accuracy of 87.2%, with an AUC-ROC score of 0.91, indicating its exceptional ability to distinguish between churned and non-churned customers. Random Forest follows closely, exhibiting robust performance, while SVM and Logistic Regression demonstrate moderate accuracy levels. This research underscores the transformative potential of machine learning in enhancing customer retention strategies within the banking industry. By identifying at-risk customers and understanding the underlying factors contributing to churn, banks can implement targeted interventions to improve customer satisfaction and loyalty. The study further suggests avenues for future research, including the exploration of real-time data analysis and the integration of qualitative customer insights, to refine predictive models and retention strategies.

Research on the Delineation Method of Urban Community Living Circle Based on Transportation Network Analysis in Yuanping, China

In the context of accelerating global urbanization and the demands for high-quality and sustainable development, the exploration of an urban community living area delineation method based on traffic network analysis offers a fresh perspective for defining urban living areas. This paper employs a traffic network analysis method rooted in objective analysis. It leverages open-source big data on Points of Interest (POI) from public facilities and road network data obtained from relevant urban planning to construct a comprehensive road network dataset for the central urban area. Following the construction of the road network dataset, the paper conducts a service area and accessibility analysis of public facilities, which serves as an initial step in outlining the boundaries of the urban community living area. Subsequently, the delineation is further refined by considering factors such as development boundaries, as well as natural and man-made constraints such as rivers, railways, and major roads. The boundaries of the urban community living area undergo further adjustments, taking into account the scope of the development boundary and various constraining elements such as rivers, railways, and main roads. A final revision is made by considering subjective factors, where the boundaries of the ten-minute urban community living area are ultimately determined through extensive communication and coordination with various stakeholders. This approach is aimed at meeting the current societal development needs, which include breaking down segregation, fostering social interactions, strengthening neighbourly relationships, enhancing overall quality of life, promoting sustainable development, and creating a more humane and sustainable city. Its ultimate goal is to enable residents to feel a sense of belonging and happiness within the bustling urban environment.

A global genome dataset for Salmonella Gallinarum recovered between 1920 and 2024

Salmonella enterica serovar Gallinarum (S. Gallinarum) is an avian-specific pathogen responsible for fowl typhoid, a severe systemic disease with high mortality in chickens. This disease poses a substantial burden to the poultry industry, particularly in developing countries like China. However, comprehensive genome datasets on S. Gallinarum are lacking. Here, we present the most extensive S. Gallinarum genome dataset, comprising 574 well-collated samples. This dataset consists of 366 genomes sequenced in our laboratory and 208 publicly available genomes, collected from various continents over the past century. Using in silico prediction, we categorized S. Gallinarum into three distinct biovars. Regarding antimicrobial resistance, 238 strains (41.5%) carried antimicrobial resistance genes (ARGs) with a total of 635 records, while 232 strains (40.4%) exhibited multi-drug resistance. Mobile genomic elements (MGEs) serve as critical drivers for ARGs. Our dataset includes 5,636 MGEs records, with most MGEs belonging to prophages and plasmids. This dataset expands our understanding of the genomic characteristics of S. Gallinarum, providing valuable resources for future genomic studies to improve disease management.