Tipping points of marine phytoplankton to multiple environmental stressors by Zhan Ban (11627098)

Data Sheet 1_Correlation between oxygenation function and laboratory indicators in COVID-19 patients based on non-enhanced chest CT images and construction of an artificial intelli... by Weiheng Kong (15372681)

“…Objective<p>By extracting early chest CT radiomic features of COVID-19 patients, we explored their correlation with laboratory indicators and oxygenation index (PaO₂/FiO₂), thereby developed an Artificial Intelligence (AI) model based on radiomic features to predict the deterioration of oxygenation function in COVID-19 patients.…”

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

Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds by Gloria Castañeda-Valencia (20758502)

“…The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. …”

Void-Center Galaxies and the Gravity of Probability Framework: Pre-DESI Consistency with VGS 12 and NGC 6789 by Jordan Waters (21620558)

“…<br><br><br><b>ORCID ID: https://orcid.org/0009-0009-0793-8089</b><br></p><p dir="ltr"><b>Code Availability:</b></p><p dir="ltr"><b>All Python tools used for GoP simulations and predictions are available at:</b></p><p dir="ltr"><b>https://github.com/Jwaters290/GoP-Probabilistic-Curvature</b><br><br>The Gravity of Probability framework is implemented in this public Python codebase that reproduces all published GoP predictions from preexisting DESI data, using a single fixed set of global parameters. …”

Albumin Degradome Foundation Atlas by Axel Petzold (7076261)

“…For each peptide, the following features are provided:</p><ul><li>Peptide identifier and coordinates (start and stop positions)</li><li>Amino acid sequence</li><li>Calculated mass-to-charge ratio (m/z)</li><li>Molecular weight (Da)</li><li>Boman index</li><li>Net charge</li><li>Isoelectric point (pI)</li><li>Hydrophobicity</li><li>Instability index</li><li>Aliphatic index</li></ul><p dir="ltr">All fragments were computationally generated using Python, with reproducible logic that mirrors the approaches used in the Neurofilament Degradome Foundation Atlas.…”

<b>Effects of Lifestyle and GLP-1RA based Interventions on Waist Circumference: A Systematic Review and Meta-Analysis</b> by Samit Ghosal (22024556)

“…</li><li><b>R scripts (00–06)</b> — reproducible code for primary, sensitivity, subgroup, and meta-regression analyses, forest plots, funnel plots, ROB2 templates, and prediction intervals.…”

LGF v11 Final Archive: EWL, GMMI, LGF-EWI Metrics and Language Spacetime Specification (Dec 2025) by heojeongbeom heo (22544705)

“…This was confirmed by the final $\mathbf{\Psi}$ Operator simulation: the Python code returned <b>$\text{nan}$</b><b> (Not a Number)</b> due to the runaway divergence of the Language Potential ($\mathbf{\Phi_L}$), serving as the <b>computational proof of the </b><b>$\mathbf{T=0}$</b><b> structural failure.…”

The data of the paper "Remote spectral detection of canopy functional strategies varying within and across forest types". by Fengqi Wu (20149158)

“…Canopy_2024_PLSR_ST_canopy_run_refit.py</p><p dir="ltr">This is the python code used for PLSR modeling.</p><p dir="ltr"><br></p><p dir="ltr">3. python_code_get_model_details.zip</p><p dir="ltr">This is the python code to get model performance, coefficients, VIPs, and so on.…”

Leveraging explainable causal artificial intelligence to study forest gross primary productivity dynamics in China's protected areas by Pedro Cabral (18947566)

“…<p dir="ltr">A Python script used for modeling forest GPP in China´s Protected Areas, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), implementation of four machine learning models to predict forest GPP, XAI and causality analysis.…”

Monte Carlo Simulation for SAPAL Framework: AI-Augmented CI/CD Reliability by Rohit Dhawan (22457026)

“…</p><p dir="ltr">Files included: <br>- simulation.py: Python simulation code <br>- README.md: Complete documentation and methodology <br><br>This code supports the paper "AI-Augmented Reliability in Continuous Integration and Deployment: A Conceptual Framework for Predictive, Adaptive, and Self-Correcting Pipelines".…”

The Transcriptional Gradient in Negative-Strand RNA Viruses Suggest a Common RNA Transcription Mechanism: Model by Jean Peccoud (275555)

“…</p><p dir="ltr">Notebook2 contains the python code for making predictions of transcriptional gradients for gene-shuffled variants.…”

Murakami et al. Supplemental Data for "Microstructural Analysis of Li-Ion Conductors with Deep Learning and SEM Images" by Kento Murakami (21260244)

“…</p><h2><b>SEM_images.zip</b>: </h2><p dir="ltr">These files consists of SEM images and numerical datasets (descriptors and objective variables) of composition, sintering temperature, and ionic conductivities for 52 samples (1-3 SEM images are included per 1 sample, total 130 images)</p><h2><b>python_codes.zip</b>: </h2><p dir="ltr">Python codes for four convolutional neural network (CNN) models used to investigate the relationship between these image data and ionic conductivity are provided.…”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>raw_data/glasgow_open_built/glasgow_open_built_areas.shp</code> - Grid defining sampling points</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python svi_module/get_svi_data.py<br></pre></pre><p dir="ltr"><b>Output:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata (IDs, coordinates)</li><li><code>svi_module/svi_data/images/</code> - Downloaded street view images</li></ul><h3>Step 2: Predict Perceptions</h3><p dir="ltr">Use pre-trained deep learning models to predict perceptual qualities (safety, beauty, liveliness, etc.) from street view images.…”

Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis by Alan Glanz (22109698)

“…</p><h2><b>Included Files</b></h2><h3><b>1. </b><code><strong>GenosophusV2.py</strong></code></h3><p dir="ltr">Executable Python implementation of the Genosophus Engine.…”

<b>Alpha-Synuclein Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…</p><p dir="ltr">Whether your work involves biomarker development, precision neurology, or machine learning, this dataset provides structured, labelled inputs that are ideal for:</p><ul><li>Training supervised models to detect or predict cleavage sites</li><li>Feature extraction from protein sequences</li><li>Clustering or classification of fragment types by mutation or disease context</li><li>Integrating with omics data for multimodal prediction tasks</li></ul><p dir="ltr">Dataset Features:</p><ul><li>Annotated α-synuclein proteolytic fragments</li><li>Includes wild-type and clinically relevant variants</li><li>Tab-delimited ASCII format for compatibility with Python, R, and ML frameworks</li><li>Linked SAS and Python scripts for pipeline reproducibility and updates</li><li>Ready-to-use for computational modelling, AI training, and bioinformatics workflows</li></ul><p dir="ltr">The dataset was generated using a reproducible codes involving Python, BLAST, and SAS. …”

SI files for "Addressing Hemolysis-Induced Loss of Sensitivity in Lateral Flow Assays of Blood Samples with Platinum-Coated Gold Nanoparticles and Machine Learning" by An Le (20473628)

“…</p><p dir="ltr">2) Model_testing.ipynb: Jupyter Notebook containing Python code for generate predictions for the 112 test strips</p><p dir="ltr">3) Best_model.keras: The final optimized machine learning model saved in Keras format.…”

Numerical analysis and modeling of water quality indicators in the Ribeirão João Leite reservoir (Goiás, Brazil) by Amanda Bueno de Moraes (22559249)

“…The code implements a statistical–computational workflow for parameter selection (VIF, Bartlett and KMO tests, PCA and FA with <i>varimax</i>) and then trains and evaluates machine-learning models to predict three key physico-chemical indicators: turbidity, true color, and total iron. …”

<b>Tau Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…</li><li><b>Thermodynamic stability</b> is predicted for each peptide, with stable candidates labelled (<code>stable=1</code>), highlighting their suitability for biomarker assay development.…”

LNP drug delivery image data by Philip Harrison (8899735)

“…</div><div><br></div><div><u>Python code:</u></div><div><a href="https://github.com/pharmbio/phil_LNP_modelling">https://github.com/pharmbio/phil_LNP_modelling</a><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><p></p>…”