<b>Optimizing Task Scheduling and Containers in Cloud Data Centers</b> by Arash GhorbanniaDelavar (22563696)

“…Overall, TPMCD provides a scalable and cost-efficient framework that enhances both <b>performance and sustainability</b> in cloud data centers.</p><p dir="ltr"><code>Original article DOI: https://doi.org/10.1016/j.jnca.2025.104132</code></p>…”

Supplementary file 1_Predicting first-time anaphylaxis in the elderly using stacked machine learning and population registers.docx by Toni Mora (8145624)

“…Key predictors included healthcare utilisation patterns, age, socioeconomic proxy (copayment level), and specific diagnostic codes related to allergic conditions.…”

Image 1_Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning.tif by Chenbo Yang (10004575)

“…Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). …”

Machine learning to predict postdialysis fatigue in patients undergoing hemodialysis by Yuhan Zhang (2792089)

“…</p><p>Seven machine learning algorithms were used to construct the prediction model, and RF performed the best.…”

<b>Dataset of GPi Deep Brain Stimulation in a Hemi-Parkinsonian rat model</b> by Mohammad Mehdi Ostad Ali Akbary (20848154)

“…The animals were kept in a controlled environment with a 12-hour light‒dark cycle and unrestricted access to food and water. …”

CSPP instance by peixiang wang (19499344)

“…</li><li>Containers are randomly assigned to different attribute groups (<code>attr_group_no</code>).</li></ul></li><li><b>Yard Location Assignment</b>:</li><li><ul><li>The script systematically assigns each container a specific location in the simulated yard, including <code>area_no</code>, <code>area_bay</code>, <code>area_row</code>, and <code>area_level</code>.…”

Supporting data for "Fracture and Non-linear Response of Biopolymer Network with Dynamic Cross-linkers" by Bingxian Tang (9151802)

“…It details the input parameters, such as line density and crosslink number, as well as the algorithms employed for filament arrangement and crosslink determination. …”

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

“…</li></ul><h3>Analysis Scripts</h3><p dir="ltr">Complete set of R scripts for reproducing all analyses:</p><ul><li><b>percent cost increase_line plot.R</b>: Creates visualizations of energy cost impacts under different conservation scenarios</li><li><b>Zonation curves.R</b>: Generates conservation performance curves and coverage statistics</li><li><b>NPV_bar_plot.R</b>: Produces economic analysis plots with Net Present Value breakdowns</li><li><b>domestic_export_map_iterations.R</b>: Creates spatial maps of renewable energy infrastructure for domestic and export scenarios</li></ul><h2>Technical Specifications</h2><h3>Data Formats</h3><ul><li><b>Spatial Data</b>: ESRI Geodatabase (.gdb), Shapefile (.shp), GeoTIFF (.tif)</li><li><b>Tabular Data</b>: CSV, Microsoft Excel (.xlsx, .xls)</li><li><b>Analysis Code</b>: R scripts (.R)</li></ul><h3>Software Requirements</h3><ul><li><b>R</b> (≥4.0.0) with packages: sf, dplyr, ggplot2, readr, readxl, tidyr, furrr, ozmaps, ggpattern</li><li><b>ESRI ArcGIS</b> or <b>QGIS</b> for geodatabase access and spatial analysis</li><li><b>Zonation</b> conservation planning software (for methodology understanding)</li></ul><h3>Hardware Recommendations</h3><ul><li><b>RAM</b>: 16GB minimum (32GB recommended for full spatial analysis)</li><li><b>Storage</b>: 15GB free space for data extraction and processing</li><li><b>CPU</b>: Multi-core processor recommended for parallel processing scripts</li></ul><h2>Detailed Description of the VRE Siting and Cost-Minimization Model</h2><p><br></p><p dir="ltr">This section provides an in-depth description of the Variable Renewable Energy (VRE) siting model, including the software, the core algorithm, and the optimisation process used to determine the least-cost, spatially constrained development trajectory for VRE infrastructure in Queensland, Australia.…”

Supplementary file 1_A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab.docx by Linlin Yan (4480570)

“…Using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms, we conducted a comprehensive analysis of lecanemab-related AEs, restricting the analysis to AEs with the role code of primary suspect (PS).…”

Data Sheet 1_Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destinat... by Mikko Strahlendorff (20458865)

“…<p>Soil wetness forecasts on a local level are needed to ensure sustainable forestry operations during summer when the soil is neither frozen nor covered with snow. …”

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

“…</p><p dir="ltr"><b>Reference:</b> <code>cluster_perceptions.py</code> script logic</p><p dir="ltr">Stages:</p><ul><li>(a) Data input</li><li>(b) K-nearest neighbours spatial weights matrix</li><li>(c) Initial agglomerative clustering</li><li>(d) Merging small clusters</li><li>(e-f) Iterative splitting of largest clusters</li></ul><h4>Figure 7: Predicted Urban Perception Scores</h4><p dir="ltr">ET cell-level perception scores across the study area.…”

Additional file 1 of The origin and evolution of cultivated rice and genomic signatures of heterosis for yield traits in super-hybrid rice by Yiyong Zhao (21466062)

“…This table delineates the results of gene expression clustering using the MFUZZ algorithm for three super-hybrid rice varieties, namely LYP9, Y900, and XLY900, along with their progenitor strains. …”

Integrating urinary metabolomics and clinical datasets for multi-cancer detection by Dongyong Lee (18786694)

“…</p><p dir="ltr">- `group_code`: short group code </p><p dir="ltr"> - e.g., `Normal`, `HTN`, `DM`, `HTN+DM`, `CRC`, `LungCA`, `PancreasCA`.…”

archive.zip by Tasneem Fatima (21976946)

“…The dataset extracted from the archive consists of a large number of labeled images of wheat grains, organized in directories such as <code>wheat_for_cnn</code>, with filenames reflecting numerical categories (e.g., <code>10_1.jpg</code>, <code>100_5.jpg</code>, <code>200_0003.jpg</code>). …”

Performance evaluation of SpaVGN on melanoma ST dataset. by Haiyan Wang (25821)

“…Color-coded regions correspond to different tissue domains. …”

Monotone Cubic B-Splines with a Neural-Network Generator by Lijun Wang (176511)

“…We evaluate our method against several existing methods, some of which do not use the monotonicity constraint, on some monotone curves with varying noise levels. We demonstrate that our method outperforms the other methods, especially in high-noise scenarios. …”

Massive Mixed Models in Julia by Phillip M. Alday (2814652)

“…<p dir="ltr">Traditional approaches to mixed effects models using generalized least squares or expectation-maximization approaches struggle to scale to datasets with many thousands of observations and hundreds of levels of a single blocking variable. Special casing of nesting or crossing of random effects is required to achieve acceptable computational performance, but this special casing often makes it very difficult to handle less-than-idealized cases, such partial crossing or multiple levels of nesting. …”

From GIS to HBIM and Back: Multiscale Performance and Condition Assessment for Networks of Public Heritage Buildings and Construction Components by Teresa Fortunato (21076099)

“…GIS-BIM data exchange routines by programming codes and algorithms are developed in Python. Dynamo “As-built” and “as-damaged” HBIM models are integrated in GIS environment multi-data seismic vulnerability assessment</p>…”

Climate anomalies due to Cerrado native vegetation loss by Argemiro Leite-Filho (10283801)

“…</li></ul><p dir="ltr"><b>Code/software</b></p><p dir="ltr">To analyze the CSV files in your dataset, you can use various software options, such as R and Microsoft Excel. …”