Showing 1 - 20 results of 942 for search '(( python model presented ) OR ( python data represent ))', query time: 0.47s Refine Results
  1. 1
  2. 2

    Presentation_1_Rule-based semi-automated tools for mapping seabed morphology from bathymetry data.pdf by Zhi Huang (472377)

    Published 2023
    “…<p>Seabed morphology maps and data are critical for knowledge-building and best practice management of marine environments. …”
  3. 3

    Python Workflow for High-Fidelity Modeling of Overland Hydrocarbon Flows with GeoClaw and Cloud Computing by Pi-Yueh Chuang (4032893)

    Published 2019
    “…In this work, we present new developments built on the open-source GeoClaw software for high-fidelity modeling of overland hydrocarbon flows, and a Python workflow for running the analysis on Microsoft Azure nodes.…”
  4. 4

    SciPy2024: Facilitating scientific investigations from long-tail data with Python by Deborah Khider (4673140)

    Published 2024
    “…Until recently, the lack of a coherent data format and the timespan of these time series (from months to millions of years) hindered the use of the scientific Python stack for their analyses. …”
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10

    Output datasets from ML–assisted bibliometric workflow in African phytochemical metabolomics research by Temitope Omogbene (18615415)

    Published 2025
    “…</li><li><b>Dataset 1B (sampled_data.xlsx):</b> A stratified random sample generated in Python for pretraining and manual annotation.…”
  11. 11
  12. 12
  13. 13

    DataSheet_1_AirSeaFluxCode: Open-source software for calculating turbulent air-sea fluxes from meteorological parameters.pdf by Stavroula Biri (14571707)

    Published 2023
    “…This means that empirical “bulk formulae” parameterizations that relate direct flux observations to concurrent measurements of the mean meteorological and sea surface variables contain considerable uncertainty. In this paper, we present a Python 3.6 (or higher) open-source software package “AirSeaFluxCode” for the computation of the heat (latent and sensible) and momentum fluxes. …”
  14. 14
  15. 15
  16. 16

    [DATASET] How do rift-related fault network distributions evolve? by Sophie Pan (12484179)

    Published 2023
    “…</p><p>(3) <b><u>Extracted fault statistics:</u></b> are the generated outputs from the Python scripts. ‘Geometric_relationships’ is the main excel database generated from ‘fault_extraction_analysis_2D.py’ whereby each row represents the data for one single fault (or label in python) and contains coordinates, strain, length etc. statistics. …”
  17. 17
  18. 18
  19. 19
  20. 20