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Showing 41 - 60 results of 117 for search 'python study implemented', query time: 0.12s Refine Results
  1. 41

    Supporting data for "Interpreting complex ecological patterns and processes across differentscales using Artificial Intelligence" by Yifei Gu (9507104)

    Published 2025
    “…<p dir="ltr">This thesis bridges the gap between field observations and the empirical understanding of ecological systems by applying AI models at different spatial scales and hierarchical levels to study ecological complexity. The detailed implementation, source code and demo dataset are included in dedicated folders for each chapter.…”
  2. 42

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

    Published 2025
    “…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
  3. 43

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

    Published 2025
    “…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
  4. 44

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

    Published 2025
    “…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
  5. 45

    The format of the electrode csv file by Joseph James Tharayil (21416715)

    Published 2025
    “…We use these tools to calculate extracellular signals from an <i>in silico</i> model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.…”
  6. 46

    The format of the simulation reports by Joseph James Tharayil (21416715)

    Published 2025
    “…We use these tools to calculate extracellular signals from an <i>in silico</i> model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.…”
  7. 47

    Comparison of BlueRecording with existing tools by Joseph James Tharayil (21416715)

    Published 2025
    “…We use these tools to calculate extracellular signals from an <i>in silico</i> model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.…”
  8. 48

    The format of the weights file by Joseph James Tharayil (21416715)

    Published 2025
    “…We use these tools to calculate extracellular signals from an <i>in silico</i> model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.…”
  9. 49

    Memory monitoring recognition test workflow. by Pedro C. Martínez-Suárez (21192459)

    Published 2025
    “…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
  10. 50

    Voice recognition workflow. by Pedro C. Martínez-Suárez (21192459)

    Published 2025
    “…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
  11. 51

    Memory monitoring recognition test main screen. by Pedro C. Martínez-Suárez (21192459)

    Published 2025
    “…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
  12. 52

    Task descriptions. by Pedro C. Martínez-Suárez (21192459)

    Published 2025
    “…</p><p>Method</p><p>The MMRT was developed using Python and Kivy, facilitating the creation of cross-platform user interfaces. …”
  13. 53

    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>…”
  14. 54

    Linking Thermal Conductivity to Equations of State Using the Residual Entropy Scaling Theory by Zhuo Li (165589)

    Published 2024
    “…To use our model easily, a software package written in Python is provided in the Supporting Information.…”
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    Table & Figure.pdfBrainwaves and Higher-Order Thinking: An EEG Study of Cognitive Engagement in Mathematics Tasks by NORLIZA BINTI MOHAMED (20739875)

    Published 2025
    “…<p>Research data refers to all materials and datasets generated, analyzed, or used in your study that are necessary to validate your findings. …”
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  18. 58

    Raw Data EEG.pdfBrainwaves and Higher-Order Thinking: An EEG Study of Cognitive Engagement in Mathematics Tasks by NORLIZA BINTI MOHAMED (20739875)

    Published 2025
    “…<p>Research data refers to all materials and datasets generated, analyzed, or used in your study that are necessary to validate your findings. …”
  19. 59

    Supporting data for "Software library to quantify the value of forecasts for decision-making: Case study on sensitivity to damages" by Laugesen et al. (2025) by Richard Laugesen (6480371)

    Published 2025
    “…<br></p><p dir="ltr">Journal paper introduces RUVPY, a Python software library which implements the Relative Utility Value (RUV) method. …”
  20. 60

    Data sets and coding scripts for research on sensory processing in ADHD and ASD by Vesko Varbanov (9687029)

    Published 2025
    “…</p><h4>Contents</h4><p dir="ltr">The repository includes:</p><ul><li>Questionnaire data (ASRS, BAPQ)</li><li>Visual orientation discrimination thresholds (vertical and oblique)</li><li>Demographic variables (age, gender)</li><li>Clinical vs. non-clinical group labels</li><li>Propensity score matching files and reproducible Python code</li><li>JASP analysis files and outputs</li><li>Study documentation and methodological details</li></ul><p dir="ltr">These data support the study’s finding that ADHD and ASD show distinct sensory signatures: clinical ADHD was associated with reduced oblique sensitivity, while clinical ASD showed enhanced vertical discrimination relative to matched non-clinical controls. …”