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

Code and data for "Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction"

Wenbin Wan, Kexin Liu, Shanlin Tong, Wu Lu, Liu Y, Xingwen Jiang, et al.

This record contains the supplementary code and data supporting the manuscript “Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction.” The archive contains raw and processed data tables, crystallographic structures, Materials…Read more

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…Read more

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…Read more

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment"

Lennart John Baals, Yiting Liu, Joerg Osterrieder, Branka Hadji Misheva

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment" This repository contains the necessary codes to reproduce results in the paper: Baals, L. J., Liu, Y., Osterrieder, J., & Hadji-Misheva, B. (2025). A Syste…Read more

Also available via: European Organization for Nuclear Research

Data used in "Multi-omics integration and batch correction using a modality-agnostic deep learning framework"

Jose Ignacio Alvira Larizgoitia, Gabriele Partel, Jelle Jacobs, Alejandro Sifrim

These are multimodal dataset objects and trained model parameters used in the study. The files are organized in pairs, where each multimodal dataset (.h5mu file) corresponds to a trained model parameter file (.pt) generated using the MIMA (Multimodal Integration with Modality-agn…Read more

Also available via: European Organization for Nuclear Research

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…Read more