<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…In total, 75 % of the labelled observations were assigned to train and 25 % to test the model. To evaluate the model performance, the root mean squared root error (RMSE, the standard deviation of the residuals that represents the mean difference between the prediction and the real value for the test set) and <i>R</i>² were used, which was calculated using r2_score() from <i>Scikit-learn</i> metrics. …”

Related to Figs 1, 2 and S1. by Tyron Chang (9922533)

Overview of deep learning terminology. by Aaron E. Maxwell (8840882)

“…This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. …”

Moulin distributions during 2016-2021 on the southwest Greenland Ice Sheet by Kang Yang (7323734)

“…</p><p><br></p><ul><li>00_Satellite-derived moulins: Moulins directly mapped from Sentinel-2 imagery, representing actual moulin positions;</li><li>01_Snapped moulins: Moulins snapped to DEM-modeled supraglacial drainage networks, primarily used for analyses;</li><li>02_Moulin recurrences: Recurring moulins determined from the snapped moulins;</li><li>03_Internally drained catchments: Internally drained catchment (IDC) associated with each moulin;</li><li>04_Surface meltwater runoff: surface meltwater runoff calculated from MAR for the study area, elevation bins, and IDCs; </li><li>05_DEM-derived: Topographic features modeled from ArcticDEM, including elevation bins, depressions and drainage networks;</li><li>06_GWR: Variables for conducting geographically weighted regression (GWR) analysis;</li></ul><p><br></p><ul><li>Code_01_Mapping moulins on the southwestern GrIS.ipynb: A Jupyter Notebook to analyze moulin distributions, reproducing most of the analyses and figures presented in the manuscript using the provided datasets;</li><li>Code_02_pre1_calculate Strain Rate from XY ice velocity.py: A preprocessing Python script to calculate strain rate for the GWR analysis;</li><li>Code_02_pre2_calculate Driving Stress from ice thickness and surface slope.py: A preprocessing Python script to calculate driving stress for the GWR analysis;</li><li>Code_02_GWR analysis.ipynb: A Jupyter Notebook to conduct the GWR analysis using the provided datasets.…”

Audio Datasets of belt conveyor rollers in mines by Juan Liu (19687435)

“…</li><li><b>Python Code: </b>This code validates the accuracy and usability of the audio feature datasets in real-time monitoring of belt conveyor roller operational states.…”

Social media images of China's terraces by Song Chen (19488280)

“…</p><p dir="ltr">These images can be used for training classification models. All code used for model training and testing is available at: https://github.com/chen7092/Deep-learning-for-cultural-ecosystem-services-of-terraces.…”

<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>Input:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata from Step 1</li><li><code>perception_module/trained_models/</code> - Pre-trained models</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python -m perception_module.pred \<br> --model-weights .…”

Supplementary material for "Euler inversion: Locating sources of potential-field data through inversion of Euler's homogeneity equation" by Leonardo Uieda (97471)

“…</p><h2>License</h2><p dir="ltr">All Python source code (including <code>.py</code> and <code>.ipynb</code> files) is made available under the MIT license. …”

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). …”

Digital Twin for Chemical Sciences by Jin Qian (19339035)

“…The procedure for generating data in Figure 3 can be found in the demo notebook in Supplementary Code. The procedure for generating data of Figure 4 has been uploaded in fig4_figshare.zip file. …”

Improving the calibration of an integrated CA-What If? digital planning framework by CA What If? (21381170)

“…</p><p dir="ltr">This dataset includes (1) all required data for reproducing the materials within the manuscript, (2) detailed Python codes of the proposed CA-What If? model, and (3) a step-by-step instruction document.…”

Attention and Cognitive Workload by Rui Varandas (11900993)

“…</p><p dir="ltr">The data for subject 2 do not include the 2nd part of the acquisition (python task) because the equipment stopped acquiring; subject 3 has the 1st (N-Back task and mental subtraction) and the 2nd part (python tutorial) together in the <code>First part</code> folder (file <code>D1_S3_PB_description.json</code> indicates the start and end of each task); subject 4 only has the mental subtraction task in the 1st part acquisition and in subject 8, the subtraction task data is included in the 2nd part acquisition, along with python task.…”

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. …”

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. …”

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

“…</p><p dir="ltr"><b>Contents:</b></p><p><b>1. Analysis Code</b></p><p>Core Python scripts used to curate metadata, process genomic data, perform lineage assignments, compute summary statistics, and prepare inputs for downstream phylogenetic and phylogeographic analyses. …”

Missing and Unaccounted-for People in Mexico (1960s–2025) by Montserrat Mora (20430644)

“…</li><li><b>Requirements File:</b> A <code>requirements.txt</code> file listing the necessary Python libraries to run the script seamlessly.…”

Building footprtints from 1970s Hexagon spy satellite images for four global urban growth hotspots by Franz Schug (10165159)

“…The data represent the final results, that means, after merging models with different chip sizes and post-processing (see manuscript). …”

Thermally Activated Resonant Tunnelling in GaAs/AlGaAs Triple Barrier Heterostructures by Craig Allford (19079081)

“…Measurements were automated using bespoke written python code.<br><br>Results are published in the article at http://iopscience.iop.org/0268-1242/30/10/105035 <br>…”

2024 HUD Point in Time Count Data by State and CoC with Serious Mental Illness and Chronic Substance Use Counts by Benjamin Gorman (21648794)

“…</p><p dir="ltr">HUD PIT Count reports for states, Washington, DC, and the 384 CoCs were systematically downloaded from the HUD Exchange website using a Python script developed using Cursor software. Cursor uses large language models, especially Claude Sonnet 4 (Anthropic), to generate code. …”

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>. …”