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

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

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

"…Combined with machine learning models, this dataset can be used for real-time diagnosis of roller operational states. …"

Moulin distributions during 2016-2021 on the southwest Greenland Ice Sheet حسب 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.…"

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation حسب Renato Soares (20348202)

"…The biochemical descriptors were then used to determine the most promising protein targets for human therapeutic approaches and environmental bioremediation using the best machine learning (ML) model. The creation of our database, coupled with the integration of computational docking protocols, represents an innovative approach to understanding the potential of cyanobacteria bioactive compounds. …"

Summary of Tourism Dataset. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Segment-wise Spending Analysis. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Hyperparameter Parameter Setting. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Marketing Campaign Analysis. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Visitor Segmentation Validation Accuracy. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Integration of VAE and RNN Architecture. حسب Jing Zhang (23775)

"…The proposed TourVaRNN integrates variational autoencoders to capture latent variables representing visitor preferences and spending habits, while recurrent neural networks model complex temporal dependencies in tourism data. …"

Image 1_An explainable analysis of depression status and influencing factors among nursing students.png حسب Yingying Li (50341)

"…Data cleaning was performed in Excel, and statistical analyses were conducted using SPSS Statistics version 27.0 and Python 3.9.</p>Results<p>The incidence of depression among nursing students is 28.60%. …"

Bacterial persistence modulates the speed, magnitude and onset of antibiotic resistance evolution حسب 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. …"

6. Motif Code Theory حسب William Terry (22279591)

"…<p dir="ltr">The Motif Code Theory (MCT) simulation code, mct_unified_code.py, is a Python 3.9 script that models the universe as a time-dependent directed multigraph G(t) = (V(t), E(t)) with N=10^7 vertices (representing quantum fields/particles) and edges (interactions). …"

Code and parameter package for long-term mapping and object-based typing of freeze–thaw colluvial deposits (1990–2024) along the Yunnan–Tibet corridor حسب Hui Ye (22308559)

"…Tested with Python 3.x/ArcGIS Pro (ArcPy) and the GEE Code Editor. …"

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> حسب 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 .…"

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

"…This is conducted through an agent based model over a population of agents that interact through a `donation game' in which resources are donated to third parties at a cost without receiving a direct benefit. …"

Daily histograms of wind speed (100m), wind direction (100m) and atmospheric stability derived from ERA5 حسب 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).…"

Hierarchical Deep Learning Framework for Automated Marine Vegetation and Fauna Analysis Using ROV Video Data حسب Bjørn Christian Weinbach (16918707)

"…</p><ol><li><b>MaskRCNN-Segmented Objects</b>:</li></ol><p dir="ltr"> - `.jpg` files representing segmented objects detected by the MaskRCNN model.…"

Modules organization over different course editions. حسب Gabriele Pozzati (21094166)

"…<p>Course editions starting from 2019 are represented side-by-side, while different working days and weeks of the same course edition are displayed vertically. …"

Datasets from the Programmatic Analysis of Fuel Treatments: from the landscape to the national level Joint Fire Science Project (14-5-01-1) حسب Douglas B. Rideout (19657906)

"…Included for each study site are individual rasters representing the fire affected resources for that study site. …"