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. …"

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

"…planning support system (PSS) sub-model to generate and analyse three representative built-up development scenarios. …"

Supplementary Material for: The prediction of hematoma growth in acute intracerebral hemorrhage: from 2-dimensional shape to 3-dimensional morphology حسب figshare admin karger (2628495)

"…We subsequently constructed the 3-dimensional morphology models, including the probability of hematoma morphology (PHM) and the probability of comprehensive model (PCM), to predict HG. …"

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. …"

Data Sheet 1_Feasibility of predicting next-day fatigue levels using heart rate variability and activity-sleep metrics in people with post-COVID fatigue.csv حسب Nana Yaw Aboagye (22637360)

"…Background<p>Post-COVID fatigue (pCF) represents a significant burden for many individuals following SARS-CoV-2 infection. …"

<b>Data and Code from 'The Perfect and Legitimate Bribe': A Transparent Record of Human-AI Collaboration in Legal Scholarship</b> حسب Joshua Stern (21748181)

"…</p><p dir="ltr">For optimal viewing of `collated-anonymized.txt`, a text editor that can handle long lines without word wrapping is recommended to preserve the indentation that represents the conversational branching structure.</p><p><br></p><p dir="ltr">### **Running Code/Software**</p><p dir="ltr">The provided scripts (`collator-ipynb.txt` and `sentence-ancestry-ipynb.txt`) are Jupyter Notebooks and require a Python 3 environment to run. …"

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).…"

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). …"

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

"…Geo-tagged images were collected using Weibo cookies and Python-based scraping tools (available at: https://github.com/dataabc/weibo-search). …"

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. …"

End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting حسب Jamie Hathaway (10285367)

"…</p><h3>policy/</h3><p dir="ltr">This folder contains pickled trajectories, in the form of a Python list.</p><p dir="ltr">The list's elements are TrajWithRew dataclass objects from the Imitation Python library (https://imitation.readthedocs.io/en/latest/)</p><p dir="ltr">TrajWithRew contains 4 main fields</p><ul><li> obs - the (unnormalised) observations, in the form of a [WINDOW_LENGTH * NUM_CHANNELS] array</li><li> acts - the actions in the form of a [WINDOW_LENGTH - 1 * NUM_ACTS] array</li><li> infos - the info values at each timestep, as a [WINDOW_LENGTH - 1] array of dicts</li><li> terminals - boolean indicating if that trajectory segment is a terminal segment</li><li> rews - the rewards as a [WINDOW_LENGTH - 1] array</li></ul><p dir="ltr">Each TrajWithRew represents not a full episodic trajectory, as is usually the case with Imitiation - rather they represent segments of a full episodic trajectory, of length WINDOW_LENGTH. …"

<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 .…"

Supplementary material for "Euler inversion: Locating sources of potential-field data through inversion of Euler's homogeneity equation" حسب 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. …"

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. حسب Enrico Bertozzi (22461709)

"…Analysis of the confusion matrix revealed a critical limitation: although the model correctly identified 785 poisonous mushrooms, it misclassified 313 as edible (false negatives), which represents an unacceptable risk in a practical application.…"

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

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