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.…”

COI reference sequences from BOLD DB by John Sundh (12853901)

“…<br>The file bold_clustered.sintax.fasta.gz is directly compatible with the SINTAX algorithm in vsearch while files bold_clustered.assignTaxonomy.fasta.gz and bold_clustered.addSpecies.fasta.gz are directly compatible with the assignTaxonomy and addSpecies functions from DADA2, respectively. …”

The software structure. by Moritz Hoffmann (6411821)

“…<p><b>(a)</b> Python user interface: Provides a Python binding to the “C++ user interface” with some additional convenience functionality. …”

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. …”

Investigation of cardiac mechanics and mechanical circulatory support therapies in peripartum cardiomyopathy using machine learning and patient-specific computational modelling by Juliet Nagawa (17333779)

“…</li></ul><p dir="ltr"> <b>ANN.zip</b></p><ul><li>Matlab and Python programs used to develop machine learning algorithms and developed machine learning models.…”

Decoding fairness motivations - repository by Sebastian Speer (6489207)

“…All analysis were conducted in Python 2.7.</div><div><br></div><div><b>Behavioral Data:</b><br></div><div><br></div><div><u>Files:</u> </div><div><br></div><div><i>DifffereceOffers.csv </i>- Offers made by participants in Study 1</div><div><i>Diffs_W.csv</i> - Offers made by participants in Study 2</div><div><br></div><div><i>Individual-differences-in-offers2.png</i> - Plot of individual differences as illustrated in the paper</div><div><i>Individual-differences-MeanOffers.png </i>- Individual differences in mean offers in both games as illustrated in the Appendix</div><div><i>SocialvsNonSocial2.png </i>- Difference in Offers between Selfish and strategic players when playing against humans and computers</div><div><br></div><div>Behavioral Data, specfically Ultimatum Game and Dictator Game Offers and Plots resulting from behavioral analysis reported in the following paper:</div><div><br></div><div>S.P.H. …”

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).…”

Code by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr"><b>RNA functional analysis</b></p><p dir="ltr">Gene Ontology (GO) analysis was performed using the Metascape website, and gene set enrichment analysis (GSEA) was conducted using the GSEABase and enrichplot packages in R. …”

Core data by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr"><b>RNA functional analysis</b></p><p dir="ltr">Gene Ontology (GO) analysis was performed using the Metascape website, and gene set enrichment analysis (GSEA) was conducted using the GSEABase and enrichplot packages in R. …”

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images by Shuyu Li (18401358)

“…</p><p dir="ltr">The dataset is freely accessible on IEEE DataPort, a data repository created by IEEE and can be found at the following URL: <a href="https://ieeexplore.ieee.org/document/10218394/algorithms?tabFilter=dataset" target="_blank">https://ieeexplore.ieee.org/document/10218394/algorithms?…”

Barro Colorado Island 50-ha plot aerial photogrammetry orthomosaics and digital surface models for 2018-2023: Globally and locally aligned time series. by Vicente Vasquez (13550731)

“…<p dir="ltr"><b>Data Citation</b></p><p dir="ltr">Please cite this dataset as follows:</p><p dir="ltr">Vásquez, Vicente, Milton García, Melvin Hernández, and Helene C. …”

VinaLigGen: a method to generate LigPlots and retrieval of hydrogen and hydrophobic interactions from protein-ligand complexes by Raghvendra Agrawal (17135479)

Skeletal_ Muscle_MRI_Registration by Lucia Fontana (9435020)

“…<p><b>MSKregPy</b> is a collection of algorithms and GUI for muscolo-skeletal image processing and registration.…”

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). …”