Data supporting the results of:"Luminescence Thermometry 2.0: Full-spectrum luminescence thermometry with machine-learning temperature read-out"

Activity cliffs resist prediction within and across protein kinases: code and derived results for a leakage-controlled machine-learning analysis

Samuel S Agboola, Oluwaseun E. Agboola, et al

Code and derived results for a study of whether the chemical transformations thatgenerate activity cliffs on one protein kinase predict cliffs on another. Matched molecular pairs were constructed from measured Ki and Kd binding affinitiesretrieved from ChEMBL (release 37) for 20…Read more

Interpretable machine-learning risk stratification at diagnosis for 3-year mortality in de novo metastatic prostate cancer (SEER): reproducibility code

Xin Wang, Guanglei Yao, Wei Ding

This archive contains the analysis code, the predictor dictionary, and the retrained primary model objects underlying the manuscript "Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality in de novo metastatic prostate cancer: developmen…Read more

Harmonized soil-erosion database and machine-learning erodibility predictor for overtopping dam-breach forecasting

Hongning Lu

It provides (1) a harmonized multi-device soil-erosion database — 1,146 specimen records from EFA, SETD, JET, HET and related devices (1,013 with critical shear stress and 972 with erodibility coefficient), 186 raw erosion-rate-versus-shear-stress curves with power-law fits, and…Read more

RAQA-AutoML: An Integrated Solution for Automated Machine Learning

Abdullah Kaviani Rad

Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, m…Read more

Chemistry-stratified reliability audit of selective abstention for universal machine-learning interatomic potentials

Zeyu Fu

Version 0.3.0 provides the sanitized, hermetic code and frozen result records supporting the chemistry-stratified selective-abstention analysis. It includes the 32-UIP roster analysis, prototype-blocked confidence intervals, mechanism and sensitivity controls, manuscript sources,…Read more

Research data supporting "Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials".

William J. Baldwin, Gábor Cśanyi, Ilyes Batatia, Martin Vondrák, Johannes T. Margraf