Code and data for reproducing the results in the original paper of DML-Geo حسب 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 sets and coding scripts for research on sensory processing in ADHD and ASD حسب 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 حسب 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). …"

The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network" حسب 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 حسب 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.…"

Comprehensive Protocol for Multimodal Image-Guided Mixed Reality Neurosurgical Navigation (MRN) Using 3D Slicer حسب Ziyu Qi (16977339)

"…</p><p dir="ltr"><b>Section I: Imaging Data Import</b></p><ul><li><b>1</b> Imaging Data Import</li><li><ul><li><b>1.1</b> CT Image Import</li><li><b>1.2</b> Structural MRI Import</li><li><b>1.3</b> Diffusion MRI Import</li></ul></li></ul><p dir="ltr"><b>Section II: Imaging Data Preprocessing</b></p><ul><li><b>2</b> Data Anonymization</li><li><ul><li><b>2.1</b> CT Data Anonymization</li><li><b>2.2</b> MRI Data Anonymization</li></ul></li><li><b>3</b> Imaging Quality Control (QC)</li><li><ul><li><b>3.1</b> CT Data Quality Assessment</li><li><b>3.2</b> Structural MRI Quality Assessment</li><li><b>3.3</b> Diffusion MRI Quality Assessment</li></ul></li><li><b>4</b> Multimodal Image Fusion</li><li><ul><li><b>4.1</b> Structural MRI Rigid Registration</li><li><b>4.2</b> Diffusion MRI Rigid Registration</li><li><b>4.3</b> Non-Rigid Image Registration</li><li><b>4.4</b> Fusion Quality Validation</li></ul></li></ul><p dir="ltr"><b>Section III: Anatomical Segmentation and Surgical Planning</b></p><ul><li><b>5</b> CT Anatomical Segmentation</li><li><ul><li><b>5.1</b> Intracranial Hemorrhage Segmentation</li><li><b>5.2</b> Ventricular System Segmentation (CT)</li><li><b>5.3</b> Paranasal Sinus Segmentation</li><li><b>5.4</b> CT Fiducial Marker Segmentation</li></ul></li><li><b>6</b> Structural MRI Anatomical Segmentation</li><li><ul><li><b>6.1</b> Brain Lesion Segmentation</li><li><b>6.2</b> Ventricular System Segmentation (MRI)</li><li><b>6.3</b> Venous Vessel Segmentation</li><li><b>6.4</b> Arterial Vessel Segmentation</li><li><b>6.5</b> Brain Tissue Segmentation</li><li><b>6.6</b> Advanced Cerebral Vessel Segmentation</li><li><b>6.7</b> MRI Fiducial Marker Segmentation</li></ul></li><li><b>7</b> White Matter Fiber Tractography</li><li><ul><li><b>7.1</b> Whole-Brain Fiber Tractography</li><li><b>7.2</b> Motor and Sensory Pathway Segmentation</li><li><b>7.3</b> Visual Pathway Segmentation</li></ul></li><li><b>8</b> Surgical Trajectory Planning</li><li><ul><li><b>8.1</b> AC-PC Alignment</li><li><b>8.2</b> Key Cranial Landmark Definition</li><li><b>8.3</b> Hematoma Aspiration Trajectory</li><li><b>8.4</b> Endoscopic Evacuation Trajectory</li><li><b>8.5</b> Craniotomy and Bone Flap Planning</li><li><b>8.6</b> External Ventricular Drainage (EVD) Planning</li><li><b>8.7</b> Advanced Surgical Path Reconstruction</li><li><b>8.8</b> Educational Burr-Hole Skull Model</li></ul></li></ul><p dir="ltr"><b>Section IV: Holographic Model Export</b></p><ul><li><b>9</b> 3D Model Generation and Export</li><li><ul><li><b>9.1</b> CT Surface Model Generation</li><li><b>9.2</b> Structural MRI Surface Model Generation</li><li><b>9.3</b> Diffusion MRI Surface Model Generation</li><li><b>9.4</b> CT Holographic Model Export</li><li><b>9.5</b> MRI Holographic Model Export</li></ul></li></ul><p dir="ltr"><b>Section V: Navigational Registration Support</b></p><ul><li><b>10</b> Fiducial Marker Localization</li><li><ul><li><b>10.1</b> CT Fiducial Marker Centroid Extraction</li><li><b>10.2</b> MRI Fiducial Marker Centroid Extraction</li></ul></li><li><b>11</b> Surface Registration Parameterization</li><li><ul><li><b>11.1</b> Facial Surface Parameterization (CT)</li><li><b>11.2</b> Facial Surface Parameterization (MRI)</li></ul></li><li><b>12</b> Laser Projection Parameterization</li><li><ul><li><b>12.1</b> Crosshair Laser Projection (CT)</li><li><b>12.2</b> Crosshair Laser Projection (MRI)</li><li><b>12.3</b> Cone Laser Projection Parameterization</li><li><b>12.4</b> Laser Projection Geometry Analysis</li></ul></li></ul><p dir="ltr"><b>Section VI: Static Digital Twins Validation</b></p><ul><li><b>13</b> Physical Validation Model Construction</li><li><ul><li><b>13.1</b> CT Physical Twin Generation</li><li><b>13.2</b> MRI Physical Twin Generation</li></ul></li><li><b>14</b> Virtual Reference Model Generation</li><li><ul><li><b>14.1</b> Virtual Reference Planes and Scalp Quadrants (CT)</li><li><b>14.2</b> Virtual Reference Planes and Scalp Quadrants (MRI)</li></ul></li></ul><p dir="ltr"><b>Section VII: Mixed Reality Navigation Accuracy Assessment</b></p><ul><li><b>15</b> Navigational Accuracy and Performance Analysis</li><li><ul><li><b>15.1</b> Accuracy Analysis via Ordered Fiducial Pairs</li><li><b>15.2</b> Registration Error Metrics Calculation (FLE and TRE)</li><li><b>15.3</b> Extrapolative Error Field and Reliability Analysis</li><li><b>15.4</b> Global Registration Error Visualization</li><li><b>15.5</b> Local Error Visualization at Anatomical Structures</li><li><b>15.6</b> Structure-Specific Registration Accuracy Analysis</li><li><b>15.7</b> Intrinsic Parameter Computation for Target Structures</li></ul></li></ul><p><br></p>…"

Spatial von-Mises Fisher Regression for Directional Data حسب Zhou Lan (11856239)

Evaluation and Statistical Analysis Code for "Multi-Task Learning for Joint Fisheye Compression and Perception for Autonomous Driving" حسب 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 حسب 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" حسب 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" حسب 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>…"

Workflow of a typical Epydemix run. حسب Nicolò Gozzi (8837522)

"…<div><p>We present Epydemix, an open-source Python package for the development and calibration of stochastic compartmental epidemic models. …"

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

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

<b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b> حسب Marly G F Costa (19812192)

"…<p dir="ltr"><b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b></p><p dir="ltr">The code was developed in the Google Collaboratory environment, using Python version 3.7.13, with TensorFlow 2.8.2. …"

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

"…Attached is the micro-emotion annotation code based on pytorch, which can be used to annotate the Goemotions dataset by yourself, or predict the emotion classification based on the annotation results. …"

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories حسب Elizaveta Mukhaleva (20602550)

"…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …"

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories حسب Elizaveta Mukhaleva (20602550)

"…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …"

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories حسب Elizaveta Mukhaleva (20602550)

"…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …"

Void Evolution at the Li/LLZO Interface: Stack Pressure and Operating Temperature-Driven Creep Effect حسب Ke Li (106849)

"…Therefore, we develop a coupled electrochemical–diffusion–mechanical (creep)-phase field for void evolution (EDMP-VE) model, describing lithium stripping and deposition, bulk and surface diffusion, creep deformation, lattice distortion, and vacancy nucleation and annihilation. …"

Void Evolution at the Li/LLZO Interface: Stack Pressure and Operating Temperature-Driven Creep Effect حسب Ke Li (106849)

"…Therefore, we develop a coupled electrochemical–diffusion–mechanical (creep)-phase field for void evolution (EDMP-VE) model, describing lithium stripping and deposition, bulk and surface diffusion, creep deformation, lattice distortion, and vacancy nucleation and annihilation. …"