Overview of generalized weighted averages. by Nobuhito Manome (8882084)

“…The code is available at <a href="https://github.com/manome/python-mab" target="_blank">https://github.com/manome/python-mab</a>.…”

Minami_etal_2025 by Keiichi Mochida (4670800)

“…</p><p dir="ltr">salmon_PDMA_RNA-seq.py: Python script used for RNA-seq read mapping with salomn to generate read counts/TPM-based gene expression dataset, which was original data for Fig5e and Fig5f.…”

<b>Code: Inferred movements of SARS-CoV-2 infections between the 25 administrative regions of Peru (Figure 3) + </b><b>International spread of four SARS-CoV-2 sub-lineages of Peruv... by Pablo Tsukayama (22614461)

“…</li><li>Random grey circles (~10,000 inhabitants) were added to represent population density, based on MINSA data.</li></ul><p dir="ltr"><b>Figure 4.…”

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

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

[ relative elevation model data visualization for Data Bloom 2024 ] by Rusmiya Aqid (22189850)

“…</p><p dir="ltr">This viz was created using QGIS and Python, based on the following dataset: U.S. Geological Survey, 20230418, USGS 1/3 Arc Second n37w113 20230418: U.S. …”

Genomic Surveillance of Pemivibart (VYD2311) Escape-Associated Mutations in SARS-CoV-2: December 2025 BioSamples (n=2) by Tahir Bhatti (20961974)

“…</p><p dir="ltr"><b>Note:</b></p><p dir="ltr">Analysis was performed using a custom Python-based bioinformatics pipeline developed for <b>high-throughput surveillance of pemivibart (VYD2311) escape mutations in SARS-CoV-2</b>. …”

Deakin IoT Traffic Dataset by Aleksandar Pasquini (13092765)

“…It enables the study and differentiation of network behaviors based on device functions and supports behavior-based profiling to identify irregular activities or potential security threats.…”

Attention and Cognitive Workload by Rui Varandas (11900993)

“…<br></p><pre><pre>import base64<br>from os import mkdir<br>from os.path import join<br><br>file = '...'…”

PepENS by Abel Chandra (16854753)

“…<p dir="ltr">PepENS is an innovative model that seamlessly integrates sequence and structure-based information with ensemble learning. It represents a pioneering, consensus-based method by combining embeddings from ProtT5-XL-UniRef50 with Position Specific Scoring Matrices and Half-Sphere Exposure features to train an ensemble model consisting of EfficientNetB0 via image output from DeepInsight technology, CatBoost, and Logistic Regression. …”

Metaverse Gait Authentication Dataset (MGAD) by sandeep ravikanti (20704127)

“…Data Structure & Format</b></h2><ul><li><b>File Format:</b> CSV</li><li><b>Number of Samples:</b> 5,000 users</li><li><b>Number of Features:</b> 16 gait-based features</li><li><b>Columns:</b> Each row represents a user with corresponding gait feature values</li><li><b>Size:</b> Approximately (mention size in MB/GB after upload)</li></ul><h2><b>3. …”

Automatically Generated Chemical KG by Connor Oryan (19851477)

“…The files are in JSON format and are intended to be loaded within Python as dictionaries. The <i>Full_SSKG.json </i>file is approximately 11GB in size when extracted. …”

Bacterial persistence modulates the speed, magnitude and onset of antibiotic resistance evolution by Giorgio Boccarella (22810952)

“…</p><p dir="ltr">Repository structure</p><p dir="ltr">Fig_1/</p><ul><li>Probability of emergence analysis</li><li>Fig_1.py: contour plot generation</li></ul><p dir="ltr">Fig_2/</p><ul><li>MIC evolution simulations</li><li>Fig_2_a/: R-based simulation analysis</li><li>Fig_2_b/: Python visualization</li><li>Fig_2_c/: speed of resistance evolution analysis</li><li>Fig_2_d/: time to resistance analysis</li></ul><p dir="ltr">Fig_3/</p><ul><li>Distribution analysis</li><li>Fig_3_a-b.R: density plots and bar charts (empirical and simulated)</li></ul><p dir="ltr">Fig_4/</p><ul><li>Mutation analysis</li><li>Fig_4_a-b/: mutation counting analysis</li><li><ul><li>Fig_4_a/: simulation data (sim)</li><li>Fig_4_b/: empirical data (emp)</li></ul></li><li>Fig_4_c/: gene ontology and functional analysis</li></ul><p dir="ltr">Fig_5/</p><ul><li>Large-scale evolutionary simulations</li><li>Fig_5_a-b/: heatmap visualizations</li><li>Fig_5_c/: MIC and extinction analysis (empirical)</li></ul><p dir="ltr">Fig_6/</p><ul><li>Population size effects</li><li>Fig_6.py: population size analysis simulations</li></ul><p dir="ltr">S1_figure/</p><ul><li>Supplementary experimental data</li></ul><p dir="ltr">S2_figure/</p><ul><li>Supplementary frequency analysis</li></ul><p dir="ltr">S3_figure/</p><ul><li>Supplementary probability analysis</li></ul><p dir="ltr">scripts_simulations_cluster/</p><ul><li>Large-scale, cluster-optimized simulations</li></ul><p dir="ltr">complete_data/</p><ul><li>Reference to the full data sheet (full data set deposited elsewhere)</li></ul><p dir="ltr">Script types and languages</p><p dir="ltr">Python scripts (.py)</p><ul><li>Mathematical modeling: survival functions, probability calculations</li><li>Stochastic simulations: tau-leaping population dynamics</li><li>Data processing: mutation analysis, frequency calculations</li><li>Visualization: plotting with matplotlib and seaborn</li><li>Typical dependencies: numpy, pandas, matplotlib, seaborn, scipy</li></ul><p dir="ltr">R scripts (.R)</p><ul><li>Statistical analysis: distribution fitting, density plots</li><li>Advanced visualization: publication-quality figures (ggplot2)</li><li>Data manipulation: dplyr / tidyr workflows</li><li>Typical dependencies: dplyr, tidyr, ggplot2, readxl, cowplot</li></ul><p dir="ltr">Data requirements</p><p dir="ltr">The scripts are designed to run using the complete_data.xlsx file and, where relevant, the raw simulation outputs and empirical data sets as described above. …”

<b>Dataset for manuscript: </b><b>Phylogenetic and genomic insights into the evolution of terpenoid biosynthesis genes in diverse plant lineages</b> by Puguang Zhao (19023065)

“…</p><p dir="ltr"> <b>3.2.2. Run Python Scripts: </b></p><p dir="ltr"> Use the following scripts based on the required analysis:</p><p dir="ltr"> Sum of expression levels: Run 'gene_expression-sum.py'.…”

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…</p><p dir="ltr">All data are stored in GeoTIFF (.tif) format and can be accessed and processed using ArcGIS, ENVI, R, and Python. Each GeoTIFF file contains grid-based predictions of habitat suitability, with values ranging from 0 to 1. …”

Genomic Epidemiology of SARS-CoV-2 in Peru from 2020 to 2024 by Pablo Tsukayama (22614461)

“…</p><p dir="ltr">Circles represent departments, scaled to the number of inferred outgoing movements.…”

Sonification of Growing Black Hole by Rose Shepherd (20328444)

“…We used the open source Python package STRAUSS to produce the sonification (Trayford and Harrison 2023). …”

Daily histograms of wind speed (100m), wind direction (100m) and atmospheric stability derived from ERA5 by Marc Imberger (6226619)

“…The following bins (left edges) have been used to create the histograms:</p><p dir="ltr">Wind speed: [0, 40) m/s (bin width 1 m/s)<br>Wind direction: [0,360) deg (bin width 15 deg)<br>Stability: 5 discrete stability classes (1: very unstable, 2: unstable, 3: neutral, 4: stable, 5: very stable)</p><p><br></p><p dir="ltr"><b>Main Purpose:</b> The dataset serves as minimum input data for the CLIMatological REPresentative PERiods (climrepper) python package (https://gitlab.windenergy.dtu.dk/climrepper/climrepper) in preparation for public release).…”

Indirect Reciprocity and the Evolution of Prejudicial Groups by Gualtiero Colombo (19078925)

“…A reputation system based on social comparison is maintained to help agent decisions to donate in order to prevent exploitation by defective individuals. …”

Methodological overview. by Jinming Xiao (12517096)

“…(B) By applying the Sparse nonnegative matrix factorization (sNMF) method, the TMS-evoked activity was decomposed into co-activation modules, representing spatially specific network components, and time-varying weights, representing temporal expressions of modules. …”

Sonification of Warming Stripes by Christopher Harrison (9448751)

“…The sonification was produced using the STRAUSS sonification Python package.</p><p dir="ltr">Here we release:<br>1. …”