Showing 61 - 80 results of 109 for search '(( python consider implementing ) OR ( python code represent ))', query time: 0.24s Refine Results
  1. 61

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

    Published 2025
    “…</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. …”
  2. 62

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

    Published 2025
    “…</li><li><b>Requirements File:</b> A <code>requirements.txt</code> file listing the necessary Python libraries to run the script seamlessly.…”
  3. 63

    MCCN Case Study 3 - Select optimal survey locality by Donald Hobern (21435904)

    Published 2025
    “…</p><p dir="ltr">This is a simple implementation that uses four environmental attributes imported for all Australia (or a subset like NSW) at a moderate grid scale:</p><ol><li>Digital soil maps for key soil properties over New South Wales, version 2.0 - SEED - see <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html" target="_blank">https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html</a></li><li>ANUCLIM Annual Mean Rainfall raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer</a></li><li>ANUCLIM Annual Mean Temperature raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer</a></li></ol><h4><b>Dependencies</b></h4><ul><li>This notebook requires Python 3.10 or higher</li><li>Install relevant Python libraries with: <b>pip install mccn-engine rocrate</b></li><li>Installing mccn-engine will install other dependencies</li></ul><h4><b>Overview</b></h4><ol><li>Generate STAC metadata for layers from predefined configuratiion</li><li>Load data cube and exclude nodata values</li><li>Scale all variables to a 0.0-1.0 range</li><li>Select four layers for comparison (soil organic carbon 0-30 cm, soil pH 0-30 cm, mean annual rainfall, mean annual temperature)</li><li>Select 10 random points within NSW</li><li>Generate 10 new layers representing standardised environmental distance between one of the selected points and all other points in NSW</li><li>For every point in NSW, find the lowest environmental distance to any of the selected points</li><li>Select the point in NSW that has the highest value for the lowest environmental distance to any selected point - this is the most different point</li><li>Clean up and save results to RO-Crate</li></ol><p><br></p>…”
  4. 64

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

    Published 2025
    “…</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.…”
  5. 65

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

    Published 2024
    “…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>…”
  6. 66

    Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

    Published 2025
    “…</p><p dir="ltr">The Python programming source code used to run the calculation of ET0 and AI is provided and available online on Figshare at:</p><p dir="ltr">https://figshare.com/articles/software/Global_Aridity_Index_and_Potential_Evapotranspiration_Climate_Database_v3_-_Algorithm_Code_Python_/20005589</p><p dir="ltr">Peer-Review Reference and Proper Citation:</p><p dir="ltr">Zomer, R.J.; Xu, J.; Trabuco, A. 2022. …”
  7. 67

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

    Published 2025
    “…</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. …”
  8. 68

    Dataset for CNN-based Bayesian Calibration of TELEMAC-2D Hydraulic Model by Jose Zevallos (21379988)

    Published 2025
    “…</li><li>Files starting with <code>y_part</code> are flattened output arrays representing corresponding water depth values.…”
  9. 69

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

    Published 2025
    “…</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>. …”
  10. 70

    CpG Signature Profiling and Heatmap Visualization of SARS-CoV Genomes: Tracing the Genomic Divergence From SARS-CoV (2003) to SARS-CoV-2 (2019) by Tahir Bhatti (20961974)

    Published 2025
    “…</p><p dir="ltr">Heatmap Images :</p><p dir="ltr">Heatmaps for CpG counts and O/E ratios comparing Wuhan-Hu-1 with its closest and most distant relatives.</p><p dir="ltr">Python Script :</p><p dir="ltr">Full Python code used for data processing, distance calculation, and heatmap generation.…”
  11. 71

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

    Published 2025
    “…Geo-tagged images were collected using Weibo cookies and Python-based scraping tools (available at: https://github.com/dataabc/weibo-search). …”
  12. 72

    Cognitive Fatigue by Rui Varandas (11900993)

    Published 2025
    “…<br></p><p dir="ltr"><b>HCI features</b> encompass keyboard, mouse, and screenshot data. Below is a Python code snippet for extracting screenshot files from the screenshots CSV file.…”
  13. 73

    <b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

    Published 2025
    “…</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 .…”
  14. 74

    Physiotherapist-Assisted Wrist Movement Protocol for EEG-Based Corticokinematic Coherence Assessment by Fanni Kovács (21733838)

    Published 2025
    “…</li></ol><h4><b>Movement File Structure</b></h4><p dir="ltr">Each entry contains:</p><ul><li><code><strong>time</strong></code>: a 32-bit unsigned integer indicating the timestamp in milliseconds,</li><li><code><strong>x</strong></code>, <code><strong>y</strong></code>, <code><strong>z</strong></code>: 16-bit signed integers representing acceleration along the respective axes,</li><li><code><strong>trigger</strong></code>: an 8-bit unsigned integer used to mark event-related triggers for synchronization with the EEG data (e.g., movement onset).…”
  15. 75

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

    Published 2025
    “…GWA-UCB1 outperformed G-UCB1, UCB1-Tuned, and Thompson sampling in most problem settings and can be useful in many situations. The code is available at <a href="https://github.com/manome/python-mab" target="_blank">https://github.com/manome/python-mab</a>.…”
  16. 76

    Minami_etal_2025 by Keiichi Mochida (4670800)

    Published 2025
    “…<h2>Code files related to Minami et al (2025)</h2><p dir="ltr">accession_plot.py:Python script used to generate Fig4a.…”
  17. 77

    Bayesian Changepoint Detection via Logistic Regression and the Topological Analysis of Image Series by Andrew M. Thomas (712104)

    Published 2025
    “…The method also successfully recovers the location and nature of changes in more traditional changepoint tasks. An implementation of our method is available in the Python package bclr.…”
  18. 78

    Satellite monitoring of Greenland wintertime buried lake drainage by Jianing Wei (22400896)

    Published 2025
    “…Buried_lake_drainage_code</p><p dir="ltr">This folder contains two Python Jupyter Notebooks for detecting wintertime buried lake drainages (BLDs). …”
  19. 79

    Table 1_Analysis of distribution equilibrium and influencing factors for older adult meal service facilities in mainland China.xlsx by Feng Wang (44414)

    Published 2025
    “…Objective<p>Analyze the distribution equilibrium of older adult meal service facilities in mainland China and explore the factors influencing their distribution.</p>Methods<p>Use Python to obtain data on older adult meal service facilities, and analyze the equity of older adult meal services using descriptive statistics, the Lorenz curve, the Gini coefficient, and the Spatial Mismatch Index (SMI). …”
  20. 80

    JASPEX model by Olugbenga OLUWAGBEMI (21403187)

    Published 2025
    “…</p><p dir="ltr">We wrote new sets of python codes and developed python programming codes to rework on the map to generate the coloured map of Southwest Nigeria from the map of Nigeria (which represented the region of our study). …”