Reproducible visualization strategies for spatially varying coefficient (SVC) models: incorporating uncertainty and assessing replicability by Victor Irekponor (22303109)

“…This study introduces <i>svc-viz</i>, an open-source Python tool that codifies best practices into a standardized framework for interpreting and communicating SVC model results. …”

OHID-FF dataset for forest fire detection and classification by xin chen (20496938)

“…</p><p dir="ltr">- Pointed to the `train val scripts/` README for model-specific commands and dependencies.</p>…”

3D PD-Controlled Nanorobot Swarm Simulation for Targeted Cancer and BBB Therapy by Umar Tabbsum (22058780)

“…Cancer-targeting nanorobots converge rapidly, while BBB-targeting nanorobots follow more complex paths due to navigation constraints.</p><p dir="ltr">Implemented in Python (NumPy, Matplotlib, 3D visualization), the framework is fully annotated and reproducible. …”

Research Database by Ana Paula Fermiano (22439479)

“…</p><p dir="ltr">Statistical analysis was conducted through <b>multiple regression models</b> implemented in <b>Jamovi</b>, supported by Geographic Information System (GIS) tools to visualize spatial patterns. …”

Mean Annual Habitat Quality and Its Driving Variables in China (1990–2018) by ChenXi Zhu (21374876)

“…</p><p dir="ltr">(HQ: Habitat Quality; CZ: Climate Zone; FFI: Forest Fragmentation Index; GPP: Gross Primary Productivity; Light: Nighttime Lights; PRE: Mean Annual Precipitation Sum; ASP: Aspect; RAD: Solar Radiation; SLOPE: Slope; TEMP: Mean Annual Temperature; SM: Soil Moisture)</p><p dir="ltr"><br>A Python script used for modeling habitat quality, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), and implementation of four machine learning models to predict habitat quality.…”

Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service) by U.S. Forest Service (17476914)

“…Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. …”

Microscopic Detection and Quantification of Microplastic Particles in Environmental Water Samples by Derek Lam (11944213)

“…Image processing algorithms, implemented in Python using adaptive thresholding techniques, were applied to segment particles from the background. …”

Comprehensive Fluid and Gravitational Dynamics Script for General Symbolic Navier-Stokes Calculations and Validation by Stylianos Touloumidis (19938747)

“…It provides a flexible foundation on which theoretical assumptions can be validated, and practical calculations performed. Implemented in Python with symbolic calculations, the script facilitates in-depth analysis of complex flow patterns and makes advanced mathematical computations more accessible. …”

Core data by Baoqiang Chen (21099509)

“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Mapping Policy Coherence in National UK Food Systems (2008– 2024): Analysing the Integration of Climate Change Mitigation and Adaptation Strategies, LEAP 2025 conference, Oxford by Ronja Teschner (20974180)

“…Van Rossum, G.; Drake, F. L. Python 3 Reference Manual, 2009.</p></td></tr></table><p></p>…”