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.…”

Accompanying data files (Melbourne, Washington DC, Singapore, and NYC-Manhattan) by Winston Yap (13771969)

“…Each folder contains the training and validation data, graph adjacency information, compiled reported energy consumption data for each city, and generated predictions.</p><p dir="ltr">Each zipped folder consists the following files:</p><ul><li>Graph data - City object nodes (.parquet) and COO format edges (.txt)</li><li>predictions.txt (model predictions from GraphSAGE model)</li><li>final_energy.parquet (Compiled training and validation building energy data)</li></ul><p dir="ltr">The provided files are supplementary to the code repository which provides Python notebooks stepping through the data preprocessing, GNN training, and satellite imagery download processes. …”

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.…”

<b>MSLU-100K: A multi-source land use dataset of Chinese major cities</b> by Yao Yao (7903457)

“…</li><li>The Manual Filtering.py-Based Multilevel Model Classification Method includes code to perform multilevel model predictions.</li></ul><h3>5.requirements.txt</h3><ul><li>Lists environment configurations and version specifications, including Python 3.7 and Pytorch 2.2.…”

Bayesian Neural Network-Based Ground Motion Model for Horizontal and V/H spectral ordinate with Epistemic Uncertainty for the European Region by jyothi yedulla (18319545)

“…<p dir="ltr">The Python code provides the computation of 100 predictions using the proposed BNN model for the given input parameters which includes Moment magnitude (M_w), Joyner-Boore distance (R_JB), Shear wave velocity (V_s30), focal depth (d) and focal mechanism.…”

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>…”

<b>Antibiotics in the Global River System Arising from Human Consumption</b> by Heloisa Ehalt Macedo (10319300)

“…</p><p dir="ltr">The data repository includes 3 datasets:</p><p dir="ltr">1. Python code: python project repository including the structure necessary for the model to run.…”

Albumin Degradome Foundation Atlas by Axel Petzold (7076261)

“…</p><h2><b>Data Format and Access</b></h2><ul><li><b>Primary file:</b> <code>Albumin_Degradome_Foundation_Atlas_v1.tar.xz</code><br>(contains all peptide tables in standard CSV format)</li><li><b>File Type:</b> ASCII comma-separated values (CSV)</li><li><b>Compression:</b> <code>xz -9 -T0</code> for maximal CPU-parallelised compression</li><li><b>Compatibility:</b></li><li><ul><li>R, Python, MATLAB, SAS</li><li>Excel, LibreOffice</li><li>Any proteomics workflow (e.g., Skyline, MaxQuant preprocessing, MS/MS spectral libraries)</li></ul></li></ul><h2><b>FAIR Principles</b></h2><p dir="ltr">This dataset is fully aligned with FAIR data standards:</p><ul><li><b>Findable:</b> Rich metadata, stable DOI, search-optimised description</li><li><b>Accessible:</b> Open-access Figshare repository</li><li><b>Interoperable:</b> Standard numeric and CSV formats</li><li><b>Reusable:</b> Transparent, reproducible Python source code included</li></ul><h2><b>Applications</b></h2><p dir="ltr">The Albumin Degradome Foundation Atlas supports research across multiple biomedical domains:</p><ul><li>Biomarker development in liver disease, kidney dysfunction, inflammation, and systemic disorders</li><li>Mass-spectrometry method development</li><li>Computational proteomics and peptide modelling</li><li>Autoimmunity and neo-epitope analysis</li><li>Protein–peptide interaction studies</li><li>Proteolytic pathway mapping and degradomics</li></ul><h2><b>Versioning and Future Work</b></h2><p dir="ltr">This is <b>Version 1</b> of the Albumin Degradome Foundation Atlas. …”

<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.…”

Judge the Votes: A System to Classify Bug Reports and Give Suggestions by Emre Dinç (21720032)

“…The data folder contains 2 files: training.csv, and bug_reports.csv. training.csv contains only 2 columns text and label, and it is the version we used when training the models. bug_reports.csv contains the columns and ids we have retrieved from Bugzilla. The code folder contains the .ipynb files we used when creating the dataset, training ML models, fine-tuning BERT-based models, and getting predictions from LLMs with both zero-shot and few-shot settings. …”

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

“…Includes imputation statistics, data dictionary, and the Python imputation code (Imputation_Air_Pollutants_NABEL.py). …”

ML for anomalous diffusion model by Yuehua Zhao (8498052)

“…<p dir="ltr"><b>ML for anomalous diffusion model</b></p><p dir="ltr">Dapeng Wang</p><p>7.24.2025</p><p dir="ltr">This repository contains the necessary codes written in Python 3 to train the classifiers.…”

Critical Planck Spin Dynamics (CPSD): A Geometric Quantum Spacetime with Zero Free Parameters by Daniel Zunzunegui (22583093)

“…Complete Python verification code included.</p><p><br></p><p dir="ltr">This work solves the hierarchy problem, explains dark energy as geometric anisotropy, and predicts the universe's ultimate fate as "Big Silence" - eternal expansion at c/φ ≈ 0.618c.…”

Synthetic Mental Health Survey Dataset by Shakil Hossain (22546658)

“…Data were generated using Python code to simulate realistic patterns and scoring based on psychological constructs.This dataset was to develop a machine learning model for predicting severity of multiple mental health disorders & overall Mental Health Status based on survey responses and psychological features.…”

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".…”

<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.…”

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.…”

Seattle Demo Accompanying Files by Winston Yap (13771969)

“…<p dir="ltr">We release the data, code, and prepared city graph objects to facilitate city scale building operating energy prediction with Seattle as a case study. …”

<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.…”

IGD-cyberbullying-detection-AI by Bryan James (19921044)

“…</p><h2>Requirements</h2><p dir="ltr">To run this code, you'll need the following dependencies:</p><ul><li>Python 3.x</li><li>TensorFlow</li><li>scikit-learn</li><li>pandas</li><li>numpy</li><li>matplotlib</li><li>imbalanced-learn</li></ul><p dir="ltr">You can install the required dependencies using the provided <code>requirements.txt</code> file.…”