Supplementary data for PCI-based pavement modelling: code, presentation, and additional analysis by Azam Amir (21505033)

Submit to AGU-Manuscript-Enhancing Landslide Displacement Prediction Using a Spatio-Temporal Deep Learning Model with Interpretable Features by Jia Wang (20526992)

“…It includes the monitoring data and model prediction results in two Excel files, along with the corresponding Python code used in the study. …”

Code and data for reproducing the results in the original paper of DML-Geo by Pengfei CHEN (8059976)

“…<p dir="ltr">This asset provides all the code and data for reproducing the results (figures and statistics) in the original paper of DML-Geo</p><h2>Main Files:</h2><p dir="ltr"><b>main.ipynb</b>: the main notebook to generate all the figures and data presented in the paper</p><p dir="ltr"><b>data_generator.py</b>: used for generating synthetic datasets to validate the performance of different models</p><p dir="ltr"><b>dml_models.py</b>: Contains implementations of different Double Machine Learning variants used in this study.…”

A model of neural population dynamics for flexible sensorimotor control by Hari Kalidindi (17123467)

Comparison of the results obtained combining NeKo and Druglogics pipeline with the original publication from [28]. by Marco Ruscone (11747264)

Table1_Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.pdf by Lijuan Liang (4277053)

“…In addition, we used Python for correlation analysis, and neural network to establish the model to distinguish whether participants experienced depression, predict the total depression score, and evaluate the effectiveness of the classification and prediction model.…”

Supporting data for "Prediction and Classification of Bacterial Virulence Factors using Deep Learning" by Wai Kai Tsui (17853245)

“…The source files contain raw data (i.e., VF and non-VF sequences), processed data, and the model training and validation scripts. The trained model is provided in a compiled standalone python package called DeepVIC. …”

Table 3_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 5_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 1_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 4_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 2_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 3_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 2_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 4_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 1_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Data sets and coding scripts for research on sensory processing in ADHD and ASD by Vesko Varbanov (9687029)

“…The repository includes raw and matched datasets, analysis outputs, and the full Python code used for the matching pipeline.</p><h4>Ethics and Approval</h4><p dir="ltr">All procedures were approved by the University of Sheffield Department of Psychology Ethics Committee (Ref: 046476). …”

Replication Package: Quantum vs. Classical Machine Learning Algorithms for Software Defect Prediction by Md Nadim (17811941)

Estimating dopamine effects on humans. by Domenico Guarino (20428000)

Striatal neurons sharpen and reverse their differences. by Domenico Guarino (20428000)