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

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

6. Motif Code Theory by 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). …”

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 by Nana Yaw Aboagye (22637360)

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

Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds by Gloria Castañeda-Valencia (20758502)

“…The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. …”

<b>Data and Code from 'The Perfect and Legitimate Bribe': A Transparent Record of Human-AI Collaboration in Legal Scholarship</b> by 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. …”

Table 3_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…</p>Conclusions<p>This study demonstrates that integrating deep learning with multi-sequence breast MRI and clinical data provides a highly effective and reliable tool for predicting HER2 expression in breast cancer. …”

Table 2_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…</p>Conclusions<p>This study demonstrates that integrating deep learning with multi-sequence breast MRI and clinical data provides a highly effective and reliable tool for predicting HER2 expression in breast cancer. …”

Table 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…</p>Conclusions<p>This study demonstrates that integrating deep learning with multi-sequence breast MRI and clinical data provides a highly effective and reliable tool for predicting HER2 expression in breast cancer. …”

Data Sheet 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…</p>Conclusions<p>This study demonstrates that integrating deep learning with multi-sequence breast MRI and clinical data provides a highly effective and reliable tool for predicting HER2 expression in breast cancer. …”

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).…”

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.…”

Comprehensive Fluid and Gravitational Dynamics Script for General Symbolic Navier-Stokes Calculations and Validation by Stylianos Touloumidis (19938747)

“…It provides a flexible foundation on which theoretical assumptions can be validated, and practical calculations performed. Implemented in Python with symbolic calculations, the script facilitates in-depth analysis of complex flow patterns and makes advanced mathematical computations more accessible. …”

World Heritage documents reveal persistent gaps between climate awareness and local action by Yang Chen (20756166)

“…The analysis section includes a GLM model implemented in R, along with evaluation tools such as correlation heatmaps, ICC agreement analysis, and MCC-based binary classification assessment. …”

Knowledge Graph validation using SHACL Shapes by Ángel Iglesias Préstamo (19745767)

“…Leveraging Rust’s performance and safety features, rudof provides efficient validation tools and Python bindings for integration with data science workflows. …”

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. …”

Hierarchical Deep Learning Framework for Automated Marine Vegetation and Fauna Analysis Using ROV Video Data by 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.…”

<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 .…”

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

“…The pipeline integrates established open-source tools (fastp, BWA-MEM, samtools, iVar, bcftools) and implements <b>codon-aware mutation calling</b> at five canonical RBD positions (R346, S371, K444, F456, F486) relative to NC_045512.2. …”

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. …”

Void-Center Galaxies and the Gravity of Probability Framework: Pre-DESI Consistency with VGS 12 and NGC 6789 by Jordan Waters (21620558)

“…<br><br><br><b>ORCID ID: https://orcid.org/0009-0009-0793-8089</b><br></p><p dir="ltr"><b>Code Availability:</b></p><p dir="ltr"><b>All Python tools used for GoP simulations and predictions are available at:</b></p><p dir="ltr"><b>https://github.com/Jwaters290/GoP-Probabilistic-Curvature</b><br><br>The Gravity of Probability framework is implemented in this public Python codebase that reproduces all published GoP predictions from preexisting DESI data, using a single fixed set of global parameters. …”