Secure Python Code Manager: A Tool for Protected Python Code Distribution and Management by Pavel Izosimov (20096259)

Python implementation of a wildfire propagation example using m:n-CAk over Z and R. by Pau Fonseca i Casas (9507338)

Python code for a rule-based NLP model for mapping circular economy indicators to SDGs by Zahir Barahmand (18008947)

Finites differences python code to solve CH equation with a source term and Comsol routine to solve Brusselator equation in radial domains. by Giulio Facchini (9031490)

Five Operator Lattice Simulation by James McDaniel (22522571)

“…</p><p dir="ltr">Running the included file <code>five_operator_lattice_sim.py</code> (Python 3.14 + NumPy 2.1) reproduces the dynamic interactions and figures reported in Appendix A of the paper, generating time-series data that demonstrate operator balance, instability, and renewal cycles.…”

The format of the weights file by Joseph James Tharayil (21416715)

The format of the electrode csv file by Joseph James Tharayil (21416715)

PTPC-UHT bounce by David Parry (22169299)

“…<br>It contains the full Python implementation of the PTPC bounce model (<code>PTPC_UHT_bounce.py</code>) and representative outputs used to generate the figures in the paper. …”

Data&Codes.zip by Zer0 Star (20545655)

Code by Baoqiang Chen (21099509)

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.…”

Data files accompanying our PLoS One publication by Peter Hinow (21810605)

Reproducible Code and Data for figures by Bonyad Ahmadi (20750327)

“…</i></p><p dir="ltr">It contains:</p><p dir="ltr">✅ <b>Python Code</b> – Scripts used for data preprocessing, and visualization.…”

The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network" by Chongshan Wan (19247614)

“…</li><li>`test_GTN.py`: Contains the code for model inference and lane extraction.</li></ul><h2>Running the Code</h2><h3><b>Data processing and feature extraction</b></h3><pre>python run_process.py</pre><p dir="ltr">This step processes trajectory data, extracts graph node features and edge features, and saves them as CSV files in the `processed_data` folder.…”

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

“…</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. …”

RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices by RealBench RealBench (22275393)

“…<br>│ │ └── uml_dag.py # UML dependency graph analysis.<br>│ ├── model_gen/ # Code generation using various LLMs.<br>│ │ ├── generate/ # LLM inference implementations.…”

<b>Code and derived data for</b><b>Training Sample Location Matters: Accuracy Impacts in LULC Classification</b> by Pajtim Zariqi (22155799)

Data and code for: Automatic fish scale analysis by Christian Vogelmann (21646472)

“…</p><h3>Includeed in this repository:</h3><ul><li><b>Raw data files:</b></li><li><code>comparison_all_scales.csv</code> – comparison_all_scales.csv - manually verified vs. automated measurements of 1095 coregonid scales</li></ul><ul><li><ul><li><code>Validation_data.csv</code> – manually measured scale data under binocular</li><li><code>Parameter_correction_numeric.csv</code> – calibration data (scale radius vs. fish length/weight)</li></ul></li><li><b>Statistical results:</b></li><li><ul><li><code>comparison_stats_core_variables.csv</code> – verification statistics (bias, relative error, limits of agreement)</li><li><code>Validation_statistics.csv</code> – summary statistics and model fits (manual vs. automated)</li></ul></li><li><b>Executable script (not GUI):</b></li><li><ul><li><code>Algorithm.py</code> – core processing module for scale feature extraction<br>→ <i>Note: The complete Coregon Analyzer application (incl. …”

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…</p><p dir="ltr">2. 28pd.py: Micro-emotion detection and annotation code based on pytorch.</p><p dir="ltr">3. 55770-1.pth: Model trained on the Goemotions dataset based on the BERT model (emotion energy level intensity is a value between 0-1).…”

Supplementary file 1_ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models.docx by Piyachat Udomwong (22563212)