Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …”

Table1_Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.pdf by Lijuan Liang (4277053)

“…We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. …”

Bayesian Calibration of the 40K Decay Scheme and its implications for 40K-based geochronology by John Carter (14649908)

“…</p><p dir="ltr">Uni_delta.xlsx - Excel Spread sheet of delta ages between 235U/2307Pb and 40Ar/39Ar and 238U/2306Pb and 40Ar/39Ar</p><p dir="ltr">Uni_Res_Time.xlsx - Excel Spread sheet of residence times for all appropriate samples</p><p dir="ltr">Uni_U_decay_constants.xlsx - Excel Spread sheet of posterior values and uncertainties for 235U and 238U decay constants</p><p dir="ltr">Uni_lam.xlsx - Summary of all posterior values for 40K decay scheme</p><p><br></p><p dir="ltr">Uni_FCs.xlsx - Summary of all posterior values for Fish Canyon sanidine </p><p><br></p><p dir="ltr">Uni_u238_Xi.xlsx - Summary of all posterior values for age perturbations for each 238U/206Pb age of each sample</p><p dir="ltr">Uni_u235_Xi.xlsx - Summary of all posterior values for age perturbations for each 235U/207Pb age of each sample</p><p><br></p><p dir="ltr">Uni_Ar_Xi.xlsx - Summary of all posterior values for age perturbations for each 40Ar/39Ar age of each sample</p><p><br></p><p dir="ltr">Uni_K40K_samples.xlsx - Summary of all posterior values for 40K/K ratios of each sample</p><p dir="ltr">Uni_K40K_standards.xlsx - Summary of all posterior values for 40K/K ratio for Fish Canyon sanidine and the 40K/K for the decay constant material</p><p dir="ltr">Potassium Isotopic Variability and implications for age.ipynb - Notebook for summary plot of potassium isotopic variability - Figures S8 and S9 in the supplementary material</p><p><br></p><p dir="ltr">Calibration Comparison Figure.ipynb - Notebook for plotting comparison of Min/Kuiper, Renne et al., and the Bayesian calibrations as a function of age. …”

A large open access dataset of transillumination imaging toward realization the optical computed tomography by To Ni Phan Van (20072530)

“…This study aimed to overcome this obstacle by introducing a comprehensive dataset of transillumination images. Methods and algorithms for generating depth-dependent point-spread function and transillumination images were presented. …”

GameOfLife Prediction Dataset by David Towers (12857447)

“…Excluding 0, the lower numbers also get increasingly unlikely, though more likely than higher numbers, we wanted to prevent gaps and therefore limited to 25 contiguous classes</p><p dir="ltr">NumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. …”

Interrupted Narratives: A Curatorial Laboratory on Digital Colonialism by Victor Murari (22208293)

“…</p><p><br></p><p dir="ltr">In a moment when algorithmic infrastructures increasingly regulate artistic visibility, the exhibition seeks to render perceptible the hidden architectures of data extraction, censorship, and surveillance that condition both creation and reception in the digital sphere. …”

Code and data for evaluating oil spill amount from text-form incident information by Yiming Liu (18823387)

“…These are separately stored in the folders “description” and “posts”.</p><h2>Algorithms for Evaluating Release Amount (RA)</h2><p dir="ltr">The algorithms are split into the following three notebooks based on their functions:</p><ol><li><b>"1_RA_extraction.ipynb"</b>:</li><li><ul><li>Identifies oil spill-related incidents from raw incident data.…”

CSPP instance by peixiang wang (19499344)

“…</b></p><p dir="ltr">Its primary function is to create structured datasets that simulate container terminal operations, which can then be used for developing, testing, and benchmarking optimization algorithms (e.g., for yard stacking strategies, vessel stowage planning).…”

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

“…Bias correction was conducted using a 25-year baseline (1990–2014), with adjustments made monthly to correct for seasonal biases. 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. …”

Code by Baoqiang Chen (21099509)

“…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 by Baoqiang Chen (21099509)

“…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). …”

Landscape17 by Vlad Carare (22092515)

“…</p><p dir="ltr">We utilized TopSearch, an open-source Python package, to perform landscape exploration, at an estimated cost of 10⁵CPUh. …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><h2>Project Structure</h2><pre><pre>Perception_based_neighbourhoods/<br>├── raw_data/<br>│ ├── ET_cells_glasgow/ # Glasgow grid cells for analysis<br>│ └── glasgow_open_built/ # Built area boundaries<br>├── svi_module/ # Street View Image processing<br>│ ├── svi_data/<br>│ │ ├── svi_info.csv # Image metadata (output)<br>│ │ └── images/ # Downloaded images (output)<br>│ ├── get_svi_data.py # Download street view images<br>│ └── trueskill_score.py # Generate TrueSkill scores<br>├── perception_module/ # Perception prediction<br>│ ├── output_data/<br>│ │ └── glasgow_perception.nc # Perception scores (demo data)<br>│ ├── trained_models/ # Pre-trained models<br>│ ├── pred.py # Predict perceptions from images<br>│ └── readme.md # Training instructions<br>└── cluster_module/ # Neighbourhood clustering<br> ├── output_data/<br> │ └── clusters.shp # Final neighbourhood boundaries<br> └── cluster_perceptions.py # Clustering algorithm<br></pre></pre><h2>Prerequisites</h2><ul><li>Python 3.8 or higher</li><li>GDAL/OGR libraries (for geospatial processing)</li></ul><h2>Installation</h2><ol><li>Clone this repository:</li></ol><p dir="ltr">Download the zip file</p><pre><pre>cd perception_based_neighbourhoods<br></pre></pre><ol><li>Install required dependencies:</li></ol><pre><pre>pip install -r requirements.txt<br></pre></pre><p dir="ltr">Required libraries include:</p><ul><li>geopandas</li><li>pandas</li><li>numpy</li><li>xarray</li><li>scikit-learn</li><li>matplotlib</li><li>torch (PyTorch)</li><li>efficientnet-pytorch</li></ul><h2>Usage Guide</h2><h3>Step 1: Download Street View Images</h3><p dir="ltr">Download street view images based on the Glasgow grid sampling locations.…”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…”

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf by Guangzong Li (16696443)

“…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …”

<b>A virtual tracer experiment to assess the temporal origin of root water uptake, evaporation, and </b><b>drainage</b> by Paolo Nasta (19710883)

“…<p dir="ltr">The temporal origin of drainage, evaporation, and root water uptake (RWU) are indicators of ecosystem functioning that shed light on how natural and anthropogenic disturbances affect plant resilience and aquifer vulnerability. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…The conidia suspension of AT13 strain (1×106 conidia/mL) was spread on PDA plates overlaid with sterile cellophane, and incubated at 25 °C under darkness. …”