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  "comment": "This dataset metadata was generated with the use of AI and reviewed by an expert. If you see any mistakes please file an issue on GitHub at https://github.com/earthcube/communityCollections/issues.",
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  "name": "G-RUN (GRUN): Global Runoff Reconstruction",
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  "description": "GRUN (Global Runoff Reconstruction; often referred to as G-RUN) is an observation-based global gridded reconstruction of monthly runoff derived using machine learning trained on in-situ streamflow observations. The dataset provides global runoff rates on a 0.5° grid over the period 1902–2014 and is intended for analyses of freshwater dynamics, hydroclimate variability, drought propagation, and evaluation of hydrological model simulations.",
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      "name": "Gionata Ghiggi",
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      "@type": "Person",
      "name": "Vincent Humphrey",
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        "name": "ETH Zurich",
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      }
    },
    {
      "@type": "Person",
      "name": "Sonia I. Seneviratne",
      "affiliation": {
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        "name": "ETH Zurich",
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      }
    },
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      "@type": "Person",
      "name": "Lukas Gudmundsson",
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      }
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      "@type": "ScholarlyArticle",
      "name": "GRUN: an observation-based global gridded runoff dataset from 1902 to 2014",
      "author": [
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          "name": "Gionata Ghiggi"
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          "name": "Vincent Humphrey"
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        {
          "@type": "Person",
          "name": "Sonia I. Seneviratne"
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      "isPartOf": {
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        "name": "Earth System Science Data"
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      "datePublished": "2019",
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