Monte Carlo Simulation for SAPAL Framework: AI-Augmented CI/CD Reliability by Rohit Dhawan (22457026)

“…<p dir="ltr">This dataset contains a Monte Carlo simulation demonstrating noise reduction in CI/CD pipelines using the Sense-Analyze-Predict-Act-Learn (SAPAL) framework. …”

Integrated experimental and techno-economic modeling of renewable natural gas production from prairie biomass by Elmin Rahic (21394722)

“…Statistical models were developed from the experimental data to predict these responses and were subsequently incorporated into a techno-economic model developed in Python using BioSTEAM. …”

DCPR_V1.0 by xiao han (21405770)

“…</p><h3><b>Configuration</b></h3><p dir="ltr">It is recommended to use the conda environment (python 3.8). See environment.yaml for details.…”

Geometric Mechanics and Stokes’ Theorem in Robotic Locomotion by Umar Tabbsum (22058780)

“…<p dir="ltr">This research develops a rigorous mathematical framework for analyzing and predicting robotic locomotion using the principles of geometric mechanics. …”

Dataset for the Modeling and Bibliometric Analysis of Business plan for Entrepreneurship by Shofie Galuh Amanda (22121604)

“…For modeling, Python was applied to generate projection analyses of annual scientific production using polynomial regression. …”

<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.</li><li><b>Python script (05_correlation_python.py)</b> — scatter plot of WC vs. …”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …”

Numerical analysis and modeling of water quality indicators in the Ribeirão João Leite reservoir (Goiás, Brazil) by Amanda Bueno de Moraes (22559249)

“…<p dir="ltr">This deposit provides the Python notebook and the input dataset used in the study “Numerical analysis and modeling of water quality indicators in the Ribeirão João Leite reservoir (Goiás, Brazil).” …”

6. Motif Code Theory by William Terry (22279591)

“…<p dir="ltr">The Motif Code Theory (MCT) simulation code, mct_unified_code.py, is a Python 3.9 script that models the universe as a time-dependent directed multigraph G(t) = (V(t), E(t)) with N=10^7 vertices (representing quantum fields/particles) and edges (interactions). …”

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)

“…<br></li></ul></li><li><b>Imputation_Air_Pollutants_NABEL.py</b> (Python Script)</li><li><ul><li>The Python code used to perform all data cleaning, imputation, plausibility checks, and calculations, generating the Imputed_AP_Data_Zurich_2010-25 sheet and associated stats/legend info from the raw data. …”

The Kidmose CANid Dataset (KCID) by Brooke Elizabeth Kidmose (13626754)

“…</p><h2>FILE TYPES</h2><p dir="ltr">The dataset provides data in three formats to support different use cases:</p><p dir="ltr"><b>.mf4 (MDF4) Format:</b> Measurement Data Format version 4 (MDF4)</p><ul><li>Binary format standardized by the Association for Standardization of Automation (ASAM)</li><li><b>Advantages:</b> Compact size, popular with automotive/CAN tools</li><li><b>Use case:</b> Native format from CSS Electronics CANEdge2</li><li><b>Reference:</b> <a href="https://www.csselectronics.com/pages/mf4-mdf4-measurement-data-format" rel="noreferrer" target="_blank">https://www.csselectronics.com/pages/mf4-mdf4-measurement-data-format</a></li></ul><p dir="ltr"><b>.log Format:</b> Text-based log format</p><ul><li><b>Compatibility:</b> Linux SocketCAN can-utils</li><li><b>Advantages:</b> Compatibility with SocketCAN can-utils; if a .log file is replayed, then data can be captured and monitored using Python's python-can library</li><li><b>References:</b> <a href="https://github.com/linux-can/can-utils" rel="noreferrer" target="_blank">https://github.com/linux-can/can-utils</a>, <a href="https://packages.debian.org/sid/can-utils" rel="noreferrer" target="_blank">https://packages.debian.org/sid/can-utils</a>, <a href="https://python-can.readthedocs.io/en/stable/" rel="noreferrer" target="_blank">https://python-can.readthedocs.io/en/stable/</a></li></ul><p dir="ltr"><b>.csv Format:</b> Text-based comma-separated values (CSV) format</p><ul><li><b>Advantages:</b> Easy to load with Python using the pandas library; easy to use with Python-based machine learning frameworks (e.g., scikit-learn, Keras, TensorFlow, PyTorch)</li><li><b>Usage:</b> Load with Python pandas: pd.read_csv()</li><li><b>Reference:</b> <a href="https://pandas.pydata.org/" rel="noreferrer" target="_blank">https://pandas.pydata.org/</a></li></ul><h2>SPECIALIZED EXPERIMENTS</h2><p dir="ltr">The KCID Dataset includes five specialized experiments:</p><p dir="ltr"><b>Fixed Routes Experiment</b></p><ul><li><b>Vehicles:</b> 2011 Chevrolet Traverse, 2017 Subaru Forester</li><li><b>Drivers:</b> male-30-55-3, male-30-55-4, male-over55-1, female-all-ages-1, female-all-ages-2, female-all-ages-5</li><li><b>Location:</b> Florida, USA (specific routes)</li><li><b>Data Collection Methods:</b> CSS Electronics CANEdge2, Kvaser Hybrid CAN-LIN</li><li><b>Purpose:</b> Capture CAN traces for specific, mappable routes; eliminate route-based variations in driver authentication data (e.g., low-speed local routes vs. high-speed long-distance routes)</li></ul><p dir="ltr"><b>OBD Requests and Responses Experiment</b></p><ul><li><b>Vehicle:</b> 2011 Chevrolet Traverse</li><li><b>Driver:</b> female-all-ages-5</li><li><b>Location:</b> Florida, USA</li><li><b>Data Collection Method:</b> CSS Electronics CANEdge2</li><li><b>Purpose:</b> Capture OBD requests and responses Arbitration IDs: <i>Requests:</i> 0x7DF, <i>Responses:</i> 0x7E8</li></ul><p dir="ltr"><b>Tire Pressure Experiment</b></p><ul><li><b>Vehicle:</b> 2011 Chevrolet Traverse</li><li><b>Driver:</b> female-all-ages-5</li><li><b>Location:</b> Florida, USA</li><li><b>Data Collection Method:</b> Kvaser Hybrid CAN-LIN</li><li><b>Purpose:</b> Capture normal and low tire pressure scenarios</li><li><b>Applications:</b> Detect tire pressure issues via CAN bus analysis; develop predictive maintenance strategies</li></ul><p dir="ltr"><b>Driving Modes and Features Experiment</b></p><ul><li><b>Vehicle:</b> 2017 Ford Focus</li><li><b>Driver:</b> male-30-55-1</li><li><b>Location:</b> Denmark</li><li><b>Data Collection Method:</b> Korlan USB2CAN</li><li><b>Purpose:</b> Capture different driving (and non-driving) modes and features</li><li><b>Examples:</b> gear (park, reverse, neutral, drive, sport); headlights on/off</li></ul><p dir="ltr"><b>Stationary Vehicles Experiment</b></p><ul><li><b>Vehicles:</b> 2024 Chevrolet Malibu, 2025 Toyota Corolla</li><li><b>Driver:</b> N/A (vehicles remained stationary)</li><li><b>Location:</b> Florida, USA</li><li><b>Data Collection Method:</b> Kvaser Hybrid CAN-LIN</li><li><b>Purpose:</b> Capture CAN bus traffic from very new, very modern vehicles; identify differences between an older vehicle's CAN bus (e.g., 2011 Chevrolet Traverse), and a newer vehicle's CAN bus (e.g., 2024 Chevrolet Malibu)</li></ul><h2>ADDITIONAL DOCUMENTATION</h2><p dir="ltr">Each "specialized experiment" directory contains a detailed README.md file with specific information about the experiment and the data collected.…”

Dataset for the Modeling and Bibliometric Analysis of E-business in Entrepreneurship (1997–2024) by Aggie Moses Zeuse (21460466)

“…For the modeling component, a predictive analysis was conducted using Python to forecast future publication volumes. …”

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

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

Global Research Dataset on Social Media in Entrepreneurial Startup (2009–2024) by Lucky Ario (22115884)

“…Visualization and analysis were conducted using Microsoft Excel for summary statistics, R Biblioshiny for thematic and trend mapping, and Python for projection modeling.…”

ML for anomalous diffusion model by Yuehua Zhao (8498052)

“…</p><p dir="ltr">5.Use the model_step3.py file to perform classification prediction using a RandomForestClassifier.…”

RNACOREX package workflow. by Aitor Oviedo-Madrid (22553399)

“…These sets are then used to build probabilistic models based on Conditional Linear Gaussian (CLG) classifiers, which allow both prediction on new samples and validation of the inferred networks.…”

Performance. by Aitor Oviedo-Madrid (22553399)

“…These sets are then used to build probabilistic models based on Conditional Linear Gaussian (CLG) classifiers, which allow both prediction on new samples and validation of the inferred networks.…”

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

“…Light predictions with the replicated model were simulated using the default model parametrization (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0330681#pone.0330681.t001" target="_blank">Table 1</a>) but for varying volume along radial direction changing the parameter xymax to with the maximum depth zmax. …”

<b>DeepB3Pred</b> by Saleh Musleh (22263718)

“…<p dir="ltr">Pipeline</p><p dir="ltr">DeepB3Pred uses the following dependencies:</p><p dir="ltr">MATLAB2018a python 3.10 numpy scipy pandas scikit-learn catboost= 1.1.1 gc_forset xgboost-1.5.0 tensorflow=1.15.0 Keras=2.1.6</p><p dir="ltr">Guiding principles:</p><p dir="ltr">The data contains a training dataset and a testing dataset. …”

DeepB3Pred - code by Saleh Musleh (22263718)

“…<p dir="ltr">DeepB3Pred </p><p dir="ltr">uses the following dependencies:</p><p dir="ltr">MATLAB2018a python 3.10 numpy scipy pandas scikit-learn catboost= 1.1.1 gc_forset xgboost-1.5.0 tensorflow=1.15.0 Keras=2.1.6</p><p dir="ltr">Guiding principles:</p><p dir="ltr">The data contains a training dataset and a testing dataset. …”