Simple implementation examples of agent AI on free energy calculation and phase-field simulation by Toshiyuki Koyama (22828581)

“…</p> <p>Using Gibbs energy calculations and diffusion simulations as examples, we demonstrated the implementation method and usefulness of simple agent AI, where sample python codes are distributed as supplemental materials.…”

Catalogue of compact radio sources in Messier-82 from e-MERLIN observations by Sibongumusa Shungube (21197363)

“…Source finding was initially performed using Python Blob Detection and Source Finder (PyBDSF).</p><p dir="ltr">The dataset includes two tables detailing the properties of these 36 sources:</p><p dir="ltr"><b>Table 3.1: CASA </b><code><strong>imfit</strong></code><b> Source Catalogue</b></p><p dir="ltr">This table contains source parameters derived using the CASA task <code>imfit</code>. …”

DeepB3Pred_source_code by Muhammad Arif (17012472)

Testing Code for JcvPCA and JsvCRP. by Océane Dubois (21989812)

“…<p>This file contains the code that implements both metrics in python and apply them on a simulated dataset.…”

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. …”

Efficient, Hierarchical, and Object-Oriented Electronic Structure Interfaces for Direct Nonadiabatic Dynamics Simulations by Sascha Mausenberger (22225772)

“…We present a novel, flexible framework for electronic structure interfaces designed for nonadiabatic dynamics simulations, implemented in Python 3 using concepts of object-oriented programming. …”

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 for "A hollow fiber membrane permeance evaluation device demonstrating outside-in and inside-out performance differences" by Timothy Warner (20222838)

“…</li><li>Plot data derived from the above data sources.</li><li>Python code to generate figures from the plot data.…”

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). …”

Code for High-quality Human Activity Intensity Maps in China from 2000-2020 by Wenqi Xie (18273238)

“…<p dir="ltr">Code and remote sensing images and interpretation results of the samples for uncertainty analysis for "High-quality Human Activity Intensity Maps in China from 2000-2020"</p><p dir="ltr">“Mapping_HAI.py”：We generated the HAI maps using ArcGIS 10.8, and the geoprocessing tasks were implemented using Python 2.7 with the ArcPy library (ArcGIS 10.8 + Python 2.7 environment). …”

Computing speed and memory usage. by David Berling (22170661)

“…<b>(b)</b> Physical memory consumption depending on simulated plane in radial and depth direction. Color coding same as in (a). Memory consumption was recorded as the maximum resident size during simulation monitored with the Python built-in module resource. …”

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

“…Each lane includes 'geometry' and 'inter_id' attributes.</li></ul><h2>Codes</h2><p dir="ltr">This repository contains the following Python codes：</p><ul><li>`data_processing.py`: Contains the implementation of data processing and feature extraction. …”

MATH_code : False Data Injection Attack Detection in Smart Grids based on Reservoir Computing by Carl-Hendrik Peters (21530624)

“…</li><li><b>3_literature_analysis_and_mapping.ipynb</b><br>Contains the Python code used for executing the systematic mapping study (SMS), including automated processing of literature data and thematic clustering.…”

Replication package for Exploring the relation between source code commit information and SonarQube issues by Liviu Marian Berciu (17797934)

“…<p dir="ltr">This item represents a replication package/dataset for the article `Exploring the relation between source code commit information and SonarQube issues`. …”

R codes and curated dataset for “EnoLEX: A Diachronic Lexical Database for the Enggano Language” by Gede Primahadi Wijaya Rajeg (14592023)

“…</p><blockquote><b><i>EnoLEX</i></b><i> represents a network of independent research materials consisting of a </i><a href="https://doi.org/10.25446/oxford.28295648.v1" rel="noreferrer" target="_blank"><i>source dataset</i></a><i>, this repository of source R codes and the curated data, the </i><a href="https://enggano.shinyapps.io/enolex/" rel="noreferrer" target="_blank"><i>online database</i></a><i>, and a conference </i><a href="https://doi.org/10.25446/oxford.27013864.v1" rel="noreferrer" target="_blank"><i>paper</i></a><i>.…”

Evaluation and Statistical Analysis Code for "Multi-Task Learning for Joint Fisheye Compression and Perception for Autonomous Driving" by Basem Ahmed (18127861)

“…</li></ul><p dir="ltr">These scripts are implemented in Python using the PyTorch framework and are provided to ensure the reproducibility of the experimental results presented in the manuscript.…”

Monte Carlo Simulation Code for Evaluating Cognitive Biases in Penalty Shootouts Using ABAB and ABBA Formats by Raul MATSUSHITA (10276562)

“…<p dir="ltr">This Python code implements a Monte Carlo simulation to evaluate the impact of cognitive biases on penalty shootouts under two formats: ABAB (alternating shots) and ABBA (similar to tennis tiebreak format). …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…The <b>innovations</b> and <b>steps</b> in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.</p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.8</p><ul><li>torch==2.0.0</li><li>torch_geometric==2.5.3</li><li>networkx==2.6.3</li><li>pyshp==2.3.1</li><li>tensorrt==8.6.1</li><li>matplotlib==3.7.2</li><li>scipy==1.10.1</li><li>scikit-learn==1.3.0</li><li>geopandas==0.13.2</li></ul><p><br></p>…”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…The <b>innovations</b> and <b>steps</b> in Case 3, including data download, sample generation, and parallel computation optimization, were independently developed and are not dependent on the GeoCube’s code.</p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.8</p><ul><li>torch==2.0.0</li><li>torch_geometric==2.5.3</li><li>networkx==2.6.3</li><li>pyshp==2.3.1</li><li>tensorrt==8.6.1</li><li>matplotlib==3.7.2</li><li>scipy==1.10.1</li><li>scikit-learn==1.3.0</li><li>geopandas==0.13.2</li></ul><p><br></p>…”

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

“…</li><li>Python/Kaggle notebooks (<code>.ipynb</code>): reproducibility pipeline for accuracy metrics and statistical analysis.…”