Comprehensive Dataset Research Articles

AI-driven universal lower-limb exoskeleton system for community ambulation.

Exoskeletons offer promising solutions for improving human mobility, but a key challenge is ensuring the controller adapts to changing walking conditions. We present an artificial intelligence (AI)-driven universal exoskeleton system that dynamically switches assistance types between walking modes, modulates assistance levels corresponding to the ground slope, and delivers assistance timely based on the current gait phase in real-time. During treadmill validation, AI-based assistance reduced metabolic cost by 6.5% compared to 3.5% for conventional assistance. We expanded testing the controller in real-world walking, where AI-based assistance showed effective modulation and higher user preference compared to conventional assistance. Leveraging the AI-based approach and a comprehensive dataset, the controller achieved superior performance in environment- and user-state estimations. This approach does not require a separate mode classifier and operates on a user-independent basis, enabling immediate deployment across diverse conditions. This study highlights the potential of AI-driven exoskeletons in facilitating human locomotion in real-world ambulation.

A comprehensive dataset on cytotoxicity of ionic liquids

Ionic liquids (ILs) are structurally tunable salts with applications ranging from chemical synthesis to batteries, novel materials and medicine. Despite their potential, the toxicity of ILs poses significant environmental and biological challenges. This study introduces a comprehensive dataset of cytotoxicity of 1227 ILs, compiled from 151 research papers and encompassing 3837 data entries. For each entry, the following information is provided: substance name, empirical formula, CAS, SMILES, molecular weight, cytotoxicity value, details on the experimental setup (incubation time, cell line, assay used, etc.), and reference to the original publication. This dataset can be used for deriving structure‒activity relationships and establishing the major structural elements that govern the cytotoxic effects of ILs on eukaryotic cells. The dataset is available freely to all researchers.

A dataset of geotechnical parameters based on international literature to characterise lithotypes in Italy

Geological and lithological maps provide essential spatial data for various environmental assessments and studies. However, these maps lack detailed quantitative information on the geotechnical characteristics of rocks and soils, which limits their use for modelling purposes. This study addresses this gap by compiling a comprehensive database of over 2300 geotechnical parameter records searching the international literature. Focusing on cohesion, friction angle, and porosity, we analyse their distributions across different lithotypes, emphasising their significance in slope stability modelling. For the Italian territory, the collected information was used to associate geotechnical parameters to the lithological classes as identified by Bucci et al. 2022. These types of reclassified maps may provide researchers and stakeholders with a comprehensive dataset useful for slope stability assessment and land management at small scale. Descriptive statistics and validation from grey literature underscore the dataset’s utility in enhancing geotechnical characterizations and supporting geological hazard assessments.

Enhancing IoNT performance with fog computing: A hybrid architecture for real-time data processing

<p>The rapid evolution of the Internet of Nano Things (IoNT) and Fog Computing presents new opportunities for creating advanced smart systems that are both efficient and responsive. Integrating IoNT with Fog Computing offers a powerful paradigm that can address the limitations of cloud-centric architectures, particularly in terms of latency, bandwidth, and real-time processing. The paper explores the synergistic combination of IoNT and Fog Computing, focusing on the development of a hybrid architecture that leverages the proximity and computational capabilities of fog nodes to process data generated by nanoscale devices. Key challenges such as resource management, data processing efficiency, and security concerns are addressed in this study. The proposed architecture not only enhances the performance of smart systems by reducing latency and optimizing resource utilization but also ensures robust security and privacy for the vast amounts of generated data. A comprehensive dataset was generated to assess the integration of the IoNT with Fog Computing, focusing on environmental parameters such as Temperature, Humidity, and Wind Speed, as collected from five IoNT sensors. Python was employed to generate and augment this dataset, ensuring the accurate representation of varied environmental conditions. The data transmission between IoNT sensors and FogNode_1 successfully demonstrated the framework’s ability to capture and process real-time environmental information. Aggregated data from the fog and cloud layers confirmed the system’s efficiency in reducing latency and maintaining data integrity. Furthermore, the implementation of advanced communication protocols and effective resource management highlights the robustness of the integration, contributing to the real-time monitoring and decision-making processes in environmental applications. As well as, this study compares Fog Computing and Cloud Computing, concluding that Fog Computing offers significant advantages in areas like latency, bandwidth utilization, resource efficiency, security, privacy, real-time processing, and edge intelligence. These benefits make Fog Computing particularly suitable for applications requiring low latency, local data processing, enhanced security, and the ability to leverage edge intelligence. While Cloud Computing may have advantages in certain areas, Fog Computing’s overall performance and versatility make it a compelling choice for those seeking to optimize their computing infrastructure. This research aims to pave the way for more resilient and intelligent smart systems that can operate effectively in various domains, including healthcare, environmental monitoring, and industrial automation.</p>

Revolutionizing market surveillance: customer relationship management with machine learning

In the telecommunications industry, predicting customer churn is essential for retaining clients and sustaining profitability. Traditional CRM systems often fall short due to their static models, limiting responsiveness to evolving customer behaviors. To address these gaps, we developed the SmartSurveil CRM model, an ensemble-based system combining random forest, gradient boosting, and support vector machine to enhance churn prediction accuracy and adaptability. Using a comprehensive telecom dataset, our model achieved high performance metrics, including an accuracy of 0.89 and ROC-AUC of 0.91, surpassing baseline approaches. Integrated into a decision support system (DSS), SmartSurveil provides actionable insights to improve customer retention, enabling telecom companies to tailor strategies dynamically. Additionally, this model addresses ethical concerns, including data privacy and algorithmic transparency, ensuring a robust and responsible CRM approach. The SmartSurveil CRM model represents a substantial advancement in predictive accuracy and practical applicability within CRM systems.

A Comprehensive Dataset of Surface Electromyography and Self-Perceived Fatigue Levels for Muscle Fatigue Analysis

Muscle fatigue is a risk factor for injuries in athletes and workers. This brings relevance to the study of this biochemical process to allow for its identification and prevention. This paper presents a novel dataset for muscle fatigue analysis comprising surface electromyography data from upper-limbs and the subject’s self-perceived fatigue level. This dataset contains 13 h and 20 min of data from 13 participants performing a total of 12 upper-limb dynamic movements (8 uni-articular and 4 complex/compound). This dataset may contribute to the testing of new fatigue detection algorithms and analysis of the underlying mechanisms.

Recovery of metagenome-assembled genomes from the rumen and fecal microbiomes of Bos indicus beef cattle

Nelore is a Bos indicus beef breed that is well-adapted to tropical environments and constitutes most of the world’s largest commercial cattle herd: the Brazilian bovine herd. Despite its significance, microbial genome recovery from ruminant microbiomes has largely excluded representatives from Brazilian Nelore cattle. To address this gap, this study presents a comprehensive dataset of microbial genomes recovered from the rumen and feces of 52 Brazilian Nelore bulls. A total of 1,526 non-redundant metagenome-assembled genomes (MAGs) were recovered from their gastrointestinal tract, with 497 ruminal and 486 fecal classified as high-quality. Phylogenetic analysis revealed that the bacterial MAGs fall into 12 phyla, with Firmicutes and Bacteroidota being the most predominant, while all archaeal MAGs belong to the genus Methanobrevibacter. The exploration of these microbial genomes will provide valuable insights into the metabolic potential and functional roles of individual microorganisms within host-microbiome interactions, contributing to a better understanding of the microbiome’s roles in bovine performance.

Have public universities in Germany become more isomorphic? Methodology and empirical test, 1995–2020

Abstract Despite being a well-known concept in neo-institutional organization theory, isomorphism (or structural similarity) has been conceptualized and empirically examined by very few studies only. This paper examines isomorphism quantitatively in German higher education, using the Relative Specialization Index (RESP). Drawing on a comprehensive data set that includes information on professorial staff, students, as well as basic and grant funding, our paper shows, first, that most variables show an isomorphic pattern, and second that German universities have become more isomorphic over time. We discuss possible explanations of this result and avenues for future research. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00340

Facial Detection and Recognition Analysis using Ontology-Driven Machine Learning Model

Facial detection and recognition technologies have achieved remarkable advancements through the integration of ontology-driven machine learning (ML). This study examines how ontology structured framework for representing domain-specific knowledge to enhance the robustness, accuracy, and interpretability of ML models, particularly Convolutional Neural Networks (CNNs), in the field of facial detection and recognition. Acknowledging the limitations of traditional ML methods, such as data inefficiency, inadequate generalization, and insufficient interpretability, this research addresses critical challenges, including semantic discrepancies, variations in environmental conditions, and ethical considerations. By leveraging ontology, the study aims to provide semantic enrichment, contextual adaptation, and data augmentation for ML models. A hybrid methodology is introduced that effectively integrates ontology with CNNs, in conjunction with the Viola-Jones and Eigenfaces algorithms, to improve performance in facial recognition tasks. The study utilizes comprehensive datasets such as CelebA and MegaFace, employing rigorous preprocessing and training processes that incorporate ontology-based feedback mechanisms. The results demonstrated significant improvements in accuracy, robustness, and explainability. Ontology-CNN models outperform traditional approaches in managing variations in facial attributes and environmental conditions. This study concludes that ontology-based frameworks present a promising avenue for developing efficient and ethically responsible facial recognition systems. Future research should focus on exploring the scalability of these models across diverse demographic contexts and further addressing ethical considerations related to privacy and fairness.

The impact of genetic variants related to the fatty acid metabolic process pathway on milk production traits in Jersey cows

The synthesis of fatty acids plays a critical role in shaping milk production characteristics in dairy cattle. Thus, identifying effective haplotypes within the fatty acid metabolism pathway will provide novel and robust insights into the genetics of dairy cattle. This study aimed to comprehensively examine the individual and combined impacts of fundamental genes within the fatty acid metabolic process pathway in Jersey cows. A comprehensive phenotypic dataset was compiled, considering milk production traits, to summarize a cow’s productivity across three lactations. Genotyping was conducted through PCR-RFLP and Sanger sequencing, while the association between genotype and phenotype was quantified using linear mixed models. Moderate biodiversity and abundant variation suitable for haplotype analysis were observed across all examined markers. The individual effects of the FABP3, LTF and ANXA9 genes significantly influenced both milk yield and milk fat production. Additionally, this study reveals novel two-way interactions between genes in the fatty acid metabolism pathway that directly affect milk fat properties. Notably, we identified that the GGAAGG haplotype in FABP3×LTF×ANXA9 interaction may be a robust genetic marker concerning both milk fat yield and percentage. Consequently, the genotype combinations highlighted in this study serve as novel and efficient markers for assessing the fat content in cow’s milk.

EVALUATING MACHINE LEARNING MODELS FOR OPTIMAL CUSTOMER SEGMENTATION IN BANKING: A COMPARATIVE STUDY

This study presents a comparative analysis of machine learning algorithms for customer segmentation in the banking sector, utilizing a comprehensive dataset that includes transactional, demographic, and engagement attributes. Various clustering models, including K-Means, Gaussian Mixture Models (GMM), Hierarchical Clustering, DBSCAN, and Spectral Clustering, were evaluated to identify the most effective approach in terms of segmentation accuracy, scalability, and interpretability. The results revealed that Spectral Clustering consistently outperformed other models, offering superior accuracy and valuable insights into customer interactions across multiple banking touchpoints. While K-Means delivered fast and scalable segmentation, it lacked the flexibility needed for non-spherical clusters. The study also highlighted the benefits of a multi-dimensional dataset approach, which provided deeper insights into customer behavior, engagement, and loyalty. Although limitations such as computational complexity and scalability challenges remain, future research should focus on real-time data integration and multi-channel interactions across banking operations. This research not only contributes to machine learning applications in banking but also offers actionable strategies for targeted marketing, personalized customer engagement, risk management, and overall service optimization.

The impact of green low-carbon finance on smog reduction and carbon mitigation: evidence from a spatial panel simultaneous equations model

ABSTRACT Mitigating smog and carbon emissions is crucial for promoting sustainable urban development. Based on a comprehensive panel dataset covering 278 Chinese cities from 2008 to 2022, this study employs dynamic spatial panel data models and dynamic panel threshold models to systematically investigate the effects of green low-carbon finance (GCF) on reducing smog and carbon emissions (RSCE), including its underlying mechanisms, spatial spillover effects, and threshold effects. The key findings are as follows: (1) The development of GCF in both local and neighboring cities significantly enhances RSCE in the respective regions. (2) While local GCF indirectly promotes RSCE through green technological innovation, the spillover effects of GCF in neighboring cities on local RSCE via green innovation are not significant. (3) There exists a notable green innovation threshold effect in the relationship between GCF and RSCE, where GCF’s impact on RSCE becomes significantly stronger after surpassing a certain level of green innovation. (4) A temporal analysis reveals that the influence of GCF on RSCE has markedly intensified as GCF-related policies have matured over time. This study provides critical policy implications for strategically leveraging GCF tools to foster high-quality, low-carbon urban development.

CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany

Abstract. Comprehensive large-sample hydrological datasets, particularly the CAMELS datasets (Catchment Attributes and MEteorology for Large-sample Studies), have advanced hydrological research and education in recent years. These datasets integrate extensive hydro-meteorological observations with landscape features, such as geology and land use, across numerous catchments within a national framework. They provide harmonised large-sample data for various purposes, such as assessing the impacts of climate change or testing hydrological models on a large number of catchments. Furthermore, these datasets are essential for the rapid progress of data-driven models in hydrology in recent years. Despite Germany's extensive hydro-meteorological measurement infrastructure, it has lacked a consistent, nationwide hydrological dataset, largely due to its decentralised management across different federal states. This fragmentation has hindered cross-state studies and made the preparation of hydrological data labour-intensive. The introduction of CAMELS-DE represents a step forward in bridging this gap. CAMELS-DE includes 1582 streamflow gauges with hydro-meteorological time series data covering up to 70 years (median length of 46 years and a minimum length of 10 years), from January 1951 to December 2020. It includes consistent catchment boundaries with areas ranging from 5 to 15 000 km2 along with detailed catchment attributes covering soil, land cover, hydrogeologic properties, and data on human influences. Furthermore, it includes a regionally trained long short-term memory (LSTM) network and a locally trained HBV (Hydrologiska Byråns Vattenbalansavdelning) model that were used as quality control and that can be used to fill gaps in discharge data or act as baseline models for the development and testing of new hydrological models. Given the large number of catchments, including numerous relatively small ones (636 catchments < 100 km2), and the time series length of up to 70 years (166 catchments with 70 years of discharge data), CAMELS-DE is one of the most comprehensive national CAMELS datasets available and offers new opportunities for research, particularly in studying long-term trends and runoff formation in small catchments and in analysing catchments with strong human influences. This article describes CAMELS-DE version 1.0, which is available at https://doi.org/10.5281/zenodo.13837553 (Dolich et al., 2024).

Topology Bench: systematic graph-based benchmarking for core optical networks

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterized by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource, and represents a 61.5% increase in spatially referenced real-world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyze both real and synthetic networks using structural, spatial, and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalizability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups based on nine optimal graph metrics using K-means. It employs a two-step optimization process: optimal features are selected by maximizing feature uniqueness through principal component analysis, and the optimal number of clusters is determined by maximizing decision boundary distances via support vector machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Dataset 1 (https://zenodo.org/records/13921775) and Code 1 (https://github.com/TopologyBench), promotes accessibility, consistency, and reproducibility.

Growth vs. green: unpacking the economic–environmental dilemma in major carbon emitters with panel ARDL analysis

Today's world is facing a criticalglobal challenge of rising temperatures and disrupted ecosystems, primarily driven by increased carbon emissions. The study aims to determine the major factors leading to carbon emissions in five major carbon-emitting countries: China, the United States, India, Russia, and Japan. Specifically, it explores the interplay between economic growth, natural resource rent (Natural Resource Rent: The economic benefit a country gains from its natural resources, such as oil or minerals, after subtracting the cost of extracting and using them), technological change, renewable energy use, and carbon emissions. The study postulates that economic growth and natural resource extraction bear a substantial interaction that contributes to carbon emissions. At the same time, technological change and the usage of renewable energy could probably become imperative to alleviate such emissions. This research, therefore, provides a good insight into the determinants of CO2 emissions in these key economies and the probable pathways toward emission reduction through innovation and harnessing of renewable sources of energy. Utilizing the comprehensive dataset between 1990 and 2021 and employing the ARDL [Panel ARDL (Autoregressive Distributed Lag): A statistical method used to analyze how different factors affect each other over time in a dataset that includes multiple subjects (e.g., countries)] model on the panel data, this study examines both the short- and long-term effects of these variables on carbon emissions, considering cross-country heterogeneity. The results underscore the significance related to guidelines for sustainable development practices and the necessity for these leading economies to lead the shift towards sustainability. By identifying factors that can mitigate emissions and enhance resilience, this research provides useful inputs to the policymakers who intend to promote renewable sources of energy usage along with technological upgrades. This study’s results contribute to the existing literature by making it a significant resource for future policy formulation and academic research in the field of mitigating climate change.

In Vitro-to-In Vivo Extrapolation on Lung Toxicity Induced by Metal Oxide Nanoparticles via Data-Mining.

While in silico analyses are commonly employed for chemical risk assessments, predicting chronic lung toxicity induced by engineered nanoparticles (ENMs) in vivo still faces many challenges due to complex interactions at multiple nanobio interfaces. In this study, we developed a rigorous method to compile published evidence on the in vivo lung toxicity of metal oxide nanoparticles (MeONPs) and revealed previously overlooked in vitro-to-in vivo extrapolation (IVIVE) relationships. A comprehensive multidimensional data set containing 1102 in vivo data points, 75 pulmonary toxicological biomarkers, and 20 features (covering in vitro effects, physicochemical properties, and exposure conditions) was constructed. An IVIVE approach that related effects at the cellular level to in vivo lung toxicity in rodent model was established with prediction accuracy reaching 89 and 80% in training and test sets. Experimental validation was conducted by testing chronic lung fibrosis of 8 new MeONPs in 32 independent mice, with prediction accuracy reaching 88%. The IVIVE model indicated that the proinflammatory cytokine IL-1β in THP-1 cells could serve as an in vitro marker to predict lung toxicity. The IVIVE model showed great promise for minimizing unnecessary animal tests and understanding toxicological mechanisms.

A Machine Learning-Based Approach for Predicting Aerodynamic Coefficients Using Deep Neural Networks and CFD Data

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted across various fields, including aerodynamics, exhibiting impressive results in complex computational processes and improving prediction accuracy. This study introduces a novel method for airfoil performance assessment through the development and training of a deep Artificial Neural Network (ANN), used for predicting aerodynamic coefficients and pressure distributions, leveraging comprehensive data obtained by using a Computational Fluid Dynamics (CFD) solver. First, an automated CFD solver was developed for obtaining the extensive dataset needed for the effective training of the ANN. The automation process consisted in the generation of a geometry and a mesh, along with the successful integration of the open-source SU2 solver for conducting the aerodynamic simulations, chosen for its versatility and straightforward integration. Once various airfoil analyses were performed and a comprehensive dataset was obtained, data was normalized and the model was trained. Throughout the training process, several model configurations were tested, varying different architectures, hyperparameters and layer settings, until the best-performing layout was chosen. After broad testing and validation, the optimal configuration was identified as being the one to demonstrate the lowest error rates and the most accurate predictions on both training and unseen data, highlighting the model’s generalization capabilities. This Machine Learning-based approach, used as a substitute for traditional methods, provides remarkable accuracy and robustness, capturing complex behaviors and significantly reducing the computational costs associated with CFD simulations.

Optimising Venturi flume oxygen transfer efficiency using uncertainty-aware decision trees

ABSTRACT This study optimizes standard oxygen transfer efficiency (SOTE) in Venturi flumes investigating the impact of key parameters such as discharge per unit width (q), throat width (W), throat length (F), upstream entrance width (E), and gauge readings (Ha and Hb). To achieve this, a comprehensive experimental dataset was analyzed using multiple linear regression (MLR), multiple nonlinear regression (MNLR), gradient boosting machine (GBM), extreme gradient boosting (XRT), random forest (RF), M5 (Pruned and Unpruned), random tree (RT), and reduced error pruning (REP). Model performance was evaluated based on key metrics: correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE). Among the proposed models, M5_Unprun emerged as the top performer, exhibiting the highest CC (0.9455), the lowest RMSE (0.1918), and the lowest MAE (0.0030). GBM followed closely with a CC value of 0.9372, an RMSE value of 0.2067, and an MAE value of 0.0006. Uncertainty analysis further solidified the superior performance of M5_Unpruned (0.7522) and GBM (0.8055), with narrower prediction bands compared to other models, including MLR, which exhibited the widest band (1.4320). One-way analysis of variance confirmed the reliability and robustness of the proposed models. Sensitivity, correlation, and SHapley Additive exPlanations analyses identified W and Hb as the most influencing factors.

Ambient vibration dataset of a short-span cable-stayed bridge

A full-scale short-span cable-stayed bridge on a wind-exposed hill in New South Wales in Australia, was instrumented to assess its dynamic response to ambient vibration. The primary goal was to generate extensive datasets for Operational Modal Analysis (OMA) and develop new Structural Health Monitoring (SHM) techniques. Wind, vehicular, and pedestrian traffic on the bridge, and highway traffic beneath it, provided sufficient ambient vibration excitation. Uni-axial accelerometers were installed on the deck and cables, and a shear strain sensor was placed at one end of the bridge to measure traffic volume. Continuous ambient temperature recording was included due to its known impact on structural modal features. This data note presents four comprehensive datasets: ‘A-Single-Day,’ ‘Seasonal,’ ‘Constant-Temperature,’ and ‘Damaged-Structure’ which correspond to different environmental, operational, and structural conditions. These datasets are intended to support researchers by providing high-quality, real-world data that can be used for validating and developing OMA frameworks and SHM techniques. This paper fills a critical gap in the literature by making these detailed datasets available for broader research applications. Detailed descriptions of the instrumentation and data collection procedures are provided, along with discussions on the validity and plausibility of the datasets.

Historical Bird Atlas and Contemporary Citizen Science Data Reveal Long‐Term Changes in Geographic Range of Kenyan Birds

ABSTRACTAimHistorical bird atlases provide comprehensive datasets for investigating long‐term changes in species' distribution. In the context of accelerating biodiversity loss, these datasets can lend critical insights into the state of bird distributions across broad spatio‐temporal scales and provide much‐needed information for impactful conservation. In Africa, the potential of atlas data to understand changes in avian populations remains largely untapped.LocationThis study mapped changes in national distribution patterns of 1088 bird species found in Kenya.MethodsTapping into one of the earliest atlas databases, this study compared Kenyan bird atlas data collected between 1970 and 1984 with recent citizen science data sourced from the Kenya Bird Map project and eBird to determine changes in ranges across 50 years. We produced maps displaying, for every 27 × 27 km square of the country, whether a species appeared, was present throughout both periods, or disappeared. We account for the change in data collection effort between the two periods by quantifying the confidence of the change for each square.ResultsThe maps produced for each species are publicly accessible through an interactive website: https://kenyabirdtrends.co.ke/. We found that related species tended to experience similar changes in their distribution ranges. The ranges of Palearctic migrants and scavengers declined drastically, while introduced birds experienced a significant range increase over the past 50 years.Main ConclusionsThis study demonstrates the potential of integrating recent citizen science data with historical atlas data to draw out the changes in range for all species at national level. The range contraction of Palearctic migrants and scavengers echoed corresponding drops in abundance at local, regional and global scales. These findings lend additional weight to the need for an increased conservation focus on migratory and scavenging birds in Kenya.