fossilbrush: An R package for automated detection and resolution of anomalies in palaeontological occurrence data by Joseph T. Flannery‐Sutherland, Nussaïbah B. Raja, Ádám T. Kocsis, Wolfgang Kiessling

“…Abstract Fossil occurrence databases are indispensable resources to the palaeontological community, yet present unique data cleaning challenges. Many studies devote significant attention to cleaning fossil occurrence data prior to analysis, but such efforts are typically bespoke and difficult to reproduce. …”
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Skeleton-Based Data Augmentation for Sign Language Recognition Using Adversarial Learning by Yuriya Nakamura, Lei Jing

“…However, it is difficult to collect sign language data, and many datasets suffer from data lack and imbalance, leading to overfitting and reduced accuracy in machine learning. …”
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Predicting wheat yield using deep learning and multi-source environmental data by Muhammad Ashfaq, Imran Khan, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…The framework employs three leading deep learning models—convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN)—trained on detrended yield data from 2017 to 2022. The Google Earth Engine platform was used to process and integrate remote sensing, climate, and soil data. …”
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Enhancing Tip Detection by Pre-Training with Synthetic Data for Ultrasound-Guided Intervention by Ruixin Wang, Jinghang Wang, Wei Zhao, Xiaohui Liu, Guoping Tan, Jun Liu, Zhiyuan Wang

“…<b>Conclusions:</b> The experimental results demonstrate that our method enhances the generalization of tip detectors without relying on expert annotations or large amounts of real data, offering significant potential for more accurate visual guidance during US-guided interventions and broader clinical applications.…”
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Streamlining of Simple Sequence Repeat Data Mining Methodologies and Pipelines for Crop Scanning by Subramaniam Geethanjali, Palchamy Kadirvel, Mahender Anumalla, Nithyananth Hemanth Sadhana, Anandan Annamalai, Jauhar Ali

“…Several bioinformatic approaches and tools have evolved to handle genomic sequences to identify SSRs and generate primers for genotyping applications in plant breeding projects. This paper includes the currently available methodologies for producing SSR markers, genomic resource databases, and computational tools/pipelines for SSR data mining and primer generation. …”
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Development and validation of a diagnostic model for tuberculous meningitis based on laboratory data by Fuyong Liu, Zheng Li, Xuejiao Li, Wei Hong, Yanlin Zhou, Yungang Han, Shuang Xia, Jiao Tan, Yunchang Yang, Shiqi Li, Zhi Li, Wenyi He, Huihui Chen, Pengxiang Li, Yali Wang, Xu Yang, Jingcai Gao, Wei Wang

“…We retrospectively collected and analyzed demographic data (gender, age) and cerebrospinal fluid (CSF) parameters, including biochemical profiles and white blood cell counts with differential analysis. …”
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Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data by Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu

“…However, traditional machine learning methods predominantly rely on supervised learning frameworks, and their dependence on large amounts of labeled samples limits practical applications. To address this challenge, we propose an innovative hybrid semisupervised model, Wasserstein GAN for galaxy classification (GC-SWGAN), designed to tackle galaxy morphology classification under conditions of limited labeled data. …”
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Research on Road Crack Detection Based on RGB-LPC-GPR Data Fusion by Z. Wang, D. Qiu, R. Wu, R. Wu, Y. Shi, W. Niu

“…Furthermore, the framework is readily extendable to infrastructure health monitoring applications, such as bridges and tunnels, providing robust technological support for intelligent road maintenance and offering data-driven insights for the long-term optimization and sustainability of urban transportation infrastructure.…”
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Evaluating Agreement Between Global Satellite Data Products for Forest Monitoring in Madagascar by Oladimeji Mudele, Marissa L. Childs, Jayden Personnat, Christopher D. Golden

“…Remote sensing data (RSD) offer accessible, globally consistent layers for forest mapping. …”
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The secondary use of the Finnish national health data repository Kanta – opportunities and obstacles by Emmi Eronen, Alpo Olavi Värri, Miika Järvinen

“…The result of the Kanta PHR use case study was that although the EU General Data Protection Regulation would allow the secondary use of the Kanta PHR data based on consent, the current Kanta Services applications do not support this at all. …”
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Enhancing epidemic preparedness: a data-driven system for managing respiratory infections by Moslem Sarani, Katayoun Jahangiri, Manoochehr Karami, Mohammadreza Honarvar

“…This study details the development of a data-driven early warning system (EWS) designed to improve outbreak detection and response utilizing the data integration and visualization capabilities of Microsoft Power BI. …”
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From pronounced to imagined: improving speech decoding with multi-condition EEG data by Denise Alonso-Vázquez, Omar Mendoza-Montoya, Ricardo Caraza, Hector R. Martinez, Javier M. Antelis

“…IntroductionImagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feedback. …”
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A Wavelet Decomposition Method for Estimating Soybean Seed Composition with Hyperspectral Data by Aviskar Giri, Vasit Sagan, Haireti Alifu, Abuduwanli Maiwulanjiang, Supria Sarkar, Bishal Roy, Felix B. Fritschi

“…These findings demonstrate that integrating handheld spectroradiometer data with wavelet transformation bridges the gap between high-end spectral imaging and practical, accessible solutions for field applications. …”
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Integrating wide-swath altimetry data into Level-4 multi-mission maps by M. Ballarotta, C. Ubelmann, V. Bellemin-Laponnaz, F. Le Guillou, G. Meda, C. Anadon, A. Laloue, A. Delepoulle, Y. Faugère, M.-I. Pujol, R. Fablet, G. Dibarboure

“…<p>Real-time observation of ocean surface topography is essential for various oceanographic applications. Historically, these observations have mainly relied on satellite nadir altimetry data, which were limited to observation scales greater than approximately 60 km. …”
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Airborne radiometric data for digital soil mapping of peat at broad and local scales by Dave O’Leary, Colin Brown, Jim Hodgson, John Connolly, Louis Gilet, Patrick Tuohy, Owen Fenton, Eve Daly

“…Validation is performed using Loss on Ignition (LOI %) point data and several different (blanket bog, raised bog, transition zone) sites in Ireland, showing classified data can detect the presence of peat soils from broad to local scales. …”
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Using machine learning to identify Parkinson’s disease severity subtypes with multimodal data by Hwayoung Park, Changhong Youm, Sang-Myung Cheon, Bohyun Kim, Hyejin Choi, Juseon Hwang, Minsoo Kim

“…This study aims to address the clinical applicability and heterogeneity of PD using PD severity subtypes classification and digital biomarker development by combining objective multimodal data with machine learning (ML) approaches. …”
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Dataset for legal question answering system in the Indian judiciary contextMendeley Data by Veningston K, Apratim Mishra

“…This ensures the dataset is well-suited for real-world legal applications, making it a valuable resource for improving AI-driven legal information retrieval systems.…”
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An approach to forecasting damage due to unfavorable circumstances associated with indistinguishability of source data by V. F. Zolotukhin, A. V. Matershev, L. A. Podkolzina

“…The solution to such problems is complicated by a large number of indicators, data uncertainty, short series of observations, incomplete initial information, insufficiently developed scientific methodological apparatus. …”
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Illusion of stability: An empirical analysis of inflation data manipulation by russia after 2022 by Alex Plastun, Anna Vorontsova, Yaroslava Slyvka, Olha Yatsenko, Liudmyla Huliaieva, Victor Sukhonos, Ruslan Bilokin

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Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging by Saurabh Bhattacharya, Sashikanta Prusty, Sanjay P. Pande, Monali Gulhane, Santosh H. Lavate, Nitin Rakesh, Saravanan Veerasamy

“…Researchers will investigate future applications of this technique to new picture kinds and clinical data.…”
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