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

"…<br><br><b>Missing-Data Handling & Imputation:</b></p><p dir="ltr">The following sequential steps were applied to create a complete and consistent daily time series suitable for analysis (presented in the Imputed_AP_Data_Zurich_2010-25 sheet), particularly addressing the absence of routine PM₂.₅ measurements prior to January 2016. The full implementation is detailed in the accompanying Python script (Imputation_Air_Pollutants_NABEL.py). …"

Research Database حسب Ana Paula Fermiano (22439479)

"…</p><p dir="ltr">Statistical analysis was conducted through <b>multiple regression models</b> implemented in <b>Jamovi</b>, supported by Geographic Information System (GIS) tools to visualize spatial patterns. …"

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

"…<p dir="ltr">This dataset accompanies the publication:<br><b>"Automatic fish scale analysis: age determination, annuli and circuli detection, length and weight back-calculation of coregonid scales"</b><br></p><p dir="ltr">It provides all essential data and statistical outputs used for the <b>verification and validation</b> of the <i>Coregon Analyzer</i> – a Python-based algorithm for automated biometric fish scale measurement.…"

Scripts for pairwise nucleotide identity graphs حسب NEIL YOUNG (1140230)

"…Next, a pairwise nucleotide distance matrix was built using a custom python script is available in this folder entitled 'distance'. …"

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

"…</li><li><b>4_final_models_pipeline.ipynb</b><br>The final implementation pipeline that loads the data, applies preprocessing and encoding (e.g., latency or ISI), trains the detection models, and stores performance metrics.…"

Online Resource: Reservoir Computing as a Promising Approach for False Data Injection Attack Detection in Smart Grids حسب Carl-Hendrik Peters (21530624)

"…</li><li><b>4_final_models_pipeline.ipynb</b><br>The final implementation pipeline that loads the data, applies preprocessing and encoding (e.g., latency or ISI), trains the detection models, and stores performance metrics.…"

(A) Sampling locations and ranges of <i>I. feisthamelii</i> (purple) and <i>I. podalirius</i> (teal) butterflies. حسب Sam Ebdon (21072525)

"…(B) Sampling locations of butterflies from the <i>Iphiclides</i> HZ. The dashed line represents the approximate HZ center, based on samples collected by Lafranchis et al. …"

Table & Figure.pdfBrainwaves and Higher-Order Thinking: An EEG Study of Cognitive Engagement in Mathematics Tasks حسب NORLIZA BINTI MOHAMED (20739875)

"…Code and Algorithms (if applicable)</p> <p><br></p> <p>Scripts for EEG signal processing and analysis</p> <p><br></p> <p>Machine learning or statistical modeling scripts</p> <p><br></p> <p>Any software implementation used to analyze brainwave patterns</p> <p><br></p> <p><br></p> <p><br></p> <p>4. …"

Raw Data EEG.pdfBrainwaves and Higher-Order Thinking: An EEG Study of Cognitive Engagement in Mathematics Tasks حسب NORLIZA BINTI MOHAMED (20739875)

"…Code and Algorithms (if applicable)</p> <p><br></p> <p>Scripts for EEG signal processing and analysis</p> <p><br></p> <p>Machine learning or statistical modeling scripts</p> <p><br></p> <p>Any software implementation used to analyze brainwave patterns</p> <p><br></p> <p><br></p> <p><br></p> <p>4. …"

OHID-FF dataset for forest fire detection and classification حسب xin chen (20496938)

"…</p><p dir="ltr">- Pointed to the `train val scripts/` README for model-specific commands and dependencies.</p>…"

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

"…[<a href="https://doi.org/10.6084/m9.figshare.27266961" rel="nofollow" target="_blank">https://doi.org/10.6084/m9.figshare.27266961</a>]</p><h2>Table of Contents</h2><ul><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#overview" target="_blank">Overview</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#requirements" target="_blank">Requirements</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#datasets" target="_blank">Datasets</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#installation" target="_blank">Installation</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#running-the-code" target="_blank">Running the Code</a></li><li><a href="https://github.com/BryanSJamesDev/IGD-cyberbullying-detection-AI#expected-results" target="_blank">Expected Results</a></li></ul><h2>Overview</h2><p dir="ltr">This repository provides the code for predicting mental health outcomes associated with Internet Gaming Disorder (IGD) and Cyberbullying using machine learning and deep learning models. Models like Logistic Regression, Random Forest, Ensemble Models, CNNs, and LSTMs are implemented to detect patterns from behavioral data.…"

MSc Personalised Medicine at Ulster University حسب Steven Watterson (100045)

"…This includes the economic models that underpin big pharma as well the importance of entrepreneurship and small medium enterprises in driving forward healthcare innovation.…"

Microscopic Detection and Quantification of Microplastic Particles in Environmental Water Samples حسب Derek Lam (11944213)

"…Image processing algorithms, implemented in Python using adaptive thresholding techniques, were applied to segment particles from the background. …"

Comprehensive Fluid and Gravitational Dynamics Script for General Symbolic Navier-Stokes Calculations and Validation حسب Stylianos Touloumidis (19938747)

"…It provides a flexible foundation on which theoretical assumptions can be validated, and practical calculations performed. Implemented in Python with symbolic calculations, the script facilitates in-depth analysis of complex flow patterns and makes advanced mathematical computations more accessible. …"

VPS13C contributes to ER-SCV contact formation. حسب Anna K. Waldmann (22250729)

"…<p>a, Representative images of random 2D TEM sections of <i>VPS13C</i> KO and control HeLa cells. …"

<b>Engineered Muscle-Derived Extracellular Vesicles Boost Insulin Sensitivity and Glucose Regulation</b> حسب Hagit Shoyhet (21090650)

"…Proteomic analyses were run with Python-V3.9.2 miRNA and protein figures were plotted using R</p>…"

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 حسب Andrew Rogers (17623239)

"…</p><h2>Software and Spatial Resolution</h2><p dir="ltr">The VRE siting model is implemented using Python and relies heavily on ArcGIS for comprehensive spatial data handling and analysis.…"

Mean Annual Habitat Quality and Its Driving Variables in China (1990–2018) حسب ChenXi Zhu (21374876)

"…</p><p dir="ltr">(HQ: Habitat Quality; CZ: Climate Zone; FFI: Forest Fragmentation Index; GPP: Gross Primary Productivity; Light: Nighttime Lights; PRE: Mean Annual Precipitation Sum; ASP: Aspect; RAD: Solar Radiation; SLOPE: Slope; TEMP: Mean Annual Temperature; SM: Soil Moisture)</p><p dir="ltr"><br>A Python script used for modeling habitat quality, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), and implementation of four machine learning models to predict habitat quality.…"

Code حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Core data حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"