January 21, 2022 Dataset Open Access

Koppa, Akash; Rains, Dominik; Hulsman, Petra; Poyatos, Rafael; Miralles, Diego G.

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  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>This repository contains the datasets used in the research article &quot;A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation&quot;.</p>\n\n<p>The repository contains the following files: 1) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 2) Output - contains the final deep learning models and the outputs (evaporation and transpiration stress factor) outputs from the hybrid model developed in the study.</p>\n\n<p>Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF file formats.</p>\n\n<p>The codes related to the research article and deep learning model are available in the following repository: https://github.com/akashkoppa/StressNet</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Hydro-Climate Extremes Lab (H-CEL), Ghent University", 
      "@id": "https://orcid.org/0000-0001-5671-0878", 
      "@type": "Person", 
      "name": "Koppa, Akash"
    }, 
    {
      "affiliation": "Hydro-Climate Extremes Lab (H-CEL), Ghent University", 
      "@id": "https://orcid.org/0000-0003-0768-4209", 
      "@type": "Person", 
      "name": "Rains, Dominik"
    }, 
    {
      "affiliation": "Hydro-Climate Extremes Lab (H-CEL), Ghent University", 
      "@id": "https://orcid.org/0000-0002-9764-3357", 
      "@type": "Person", 
      "name": "Hulsman, Petra"
    }, 
    {
      "affiliation": "CREAF, E08193 Bellaterra (Cerdanyola del Vall\u00e8s), Catalonia, Spain", 
      "@id": "https://orcid.org/0000-0003-0521-2523", 
      "@type": "Person", 
      "name": "Poyatos, Rafael"
    }, 
    {
      "affiliation": "Hydro-Climate Extremes Lab (H-CEL), Ghent University", 
      "@id": "https://orcid.org/0000-0001-6186-5751", 
      "@type": "Person", 
      "name": "Miralles, Diego G."
    }
  ], 
  "url": "https://zenodo.org/record/5886608", 
  "datePublished": "2022-01-21", 
  "keywords": [
    "Deep learning", 
    "Hybrid Modeling", 
    "Evaporation", 
    "Evaporative Stress", 
    "Transpiration", 
    "GLEAM", 
    "Machine Learning", 
    "Earth System Modeling"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/9fde1609-b011-43c5-b55a-478422a180ed/data_figures_hybrid_paper.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
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      "contentUrl": "https://zenodo.org/api/files/9fde1609-b011-43c5-b55a-478422a180ed/output_hybrid_model_output.zip", 
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      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.5886608", 
  "@id": "https://doi.org/10.5281/zenodo.5886608", 
  "@type": "Dataset", 
  "name": "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation"
}