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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23Cited by 0

Deciphering the Host-Range Grammar of Orthoflaviviruses Using Foundation Model Embeddings: A Leakage-Aware Evaluation Framework — Data and Code

Brhanu F. Znabu, Qiuming Yao, Nicole R. Sexton

Data and code for a leakage-aware evaluation of machine-learning predictors of orthoflavivirus host range. Contains the full analysis pipeline, DNABERT-2 embeddings, window-level sequence data, results, and figure-generation scripts to reproduce every figure and result. verify_reproducibility.py checks all key manuscript values.

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-29

Data for Cognitive Digital Twin Framework in "The Spine", Madinaty

Shimaa Elgingihy

This dataset contains the research data, code, and validation files associated with the paper titled: "A Cognitive Digital Twin Framework for Sustainable Urban Water Management and Carbon Sequestration: A Case Study of 'The Spine', Madinaty, Cairo, Egypt" Authors Shimaa M. Elging…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

Code and data for: Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

Basri Kerem Alhan, Kenessary Khabat

Code, processed data products, configuration, and results artifacts for "Machine learning versus ETAS for earthquake forecasting in the Sea of Marmara: a leakage-audited negative result and a closed-form scoring artifact" (Alhan & Khabat, submitted to Seismica). Version 1.2.0 acc…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

Data and Code for: Graph convolutional network model of CD4+ T cells provides an optimal single-cell clock for human age prediction

Qingqing Hao, Jun Zhang, M H Zhao, Min Wang, Fanglin Guan, Jiangwei Yan

OverviewThis repository contains the code and processed datasets for the manuscript: “Graph convolutional network model of CD4+ T cells provides an optimal single-cell clock for human age prediction”.This study demonstrates that utilizing single-cell Graph Convolutional Networks…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

YOLO vs. Diffusion Networks for Underground Pipe Detection: A Case Study Using Ground Penetrating Radar Data

Doaa Senousy, Omar Saad, Shereen Ebrahim, Abbas Abbas, Amr Gody

This repository contains the official open-source code for [YOLO vs. Diffusion Networks for Underground PipeDetection: A Case Study Using Ground PenetratingRadar Data]. ### OverviewThis software provides an end-to-end implementation of deep Learning for Pipeline Detection Using G…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26

Data and Code Supporting "Enhancing Urban Flood Risk Assessment: A PCA-Integrated Deep Learning Surrogate for Hazard and Damage Prediction"

Hyeon‐Tae Moon, G. Kim

This record provides the processed data and Version 1.0 of the analysis code supporting the study “Enhancing Urban Flood Risk Assessment: A PCA-Integrated Deep Learning Surrogate for Hazard and Damage Prediction.” The archive includes the synthetic rainfall–inundation–damage data…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

Leakage-Safe Evaluation of Sensor-Failure Robustness in Dynamic Gas Mixture Quantification

Bakti Dwi Waluyo, Muhammad Aulia Rahman Sembiring

This repository contains the complete execution pipeline for the study: "Leakage-Safe Evaluation of Stochastic Channel Masking for Sensor-Failure Robustness in Dynamic Gas Mixture Quantification." The code provides an end-to-end reproducible workflow for processing the UCI Gas Se…

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