{"@type": "dcat:Dataset", "DOI": "10.15121/1869828", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Dimitrios Ioannis Belivanis", "hasEmail": "mailto:dbelivan@stanford.edu"}, "dataQuality": true, "description": "Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1377/AR_dataset_3D_diff.hdf5", "description": "Dataset of type HDF5 for training NN (neural network). The Parameters Documentation resource in this submission outlines the input and output parameters of the dataset.", "format": "hdf5", "mediaType": "application/octet-stream", "title": "AR Training Dataset 3D_diff.hdf5"}, {"@type": "dcat:Distribution", "accessURL": "https://gdr.openei.org/files/1377/ParametersDocumentation.txt", "description": "This documentation explains the organization of the AR Training Dataset for the GeoThermalCloud Neural Network. Parameters detailed include the input and output parameters.", "format": "txt", "mediaType": "text/plain", "title": "Parameters Documentation.txt"}, {"@type": "dcat:Distribution", "accessURL": "https://github.com/SmartTensors/GeoThermalCloud.jl", "description": "Geothermal Cloud for Machine Learning. Includes the code used in the GeoThermalCloud Project.", "format": "jl", "mediaType": "application/octet-stream", "title": "GeoThermalCloud GitHub"}], "identifier": "https://data.openei.org/submissions/7488", "issued": "2022-04-04T06:00:00Z", "keyword": ["AI", "artificial intelligence", "development", "discovery", "energy", "exploration", "geothermal", "hidden geothermal resources", "machine learning", "model", "modeling", "neural network", "processed data", "remote sensing", "resource", "resource detection", "training data", "training dataset"], "landingPage": "https://gdr.openei.org/submissions/1377", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2022-05-26T16:04:57Z", "programCode": ["019:006"], "projectLead": "Mike Weathers", "projectNumber": "35514", "projectTitle": "Thermo-hydro-chemical data for machine learning model development", "publisher": {"@type": "org:Organization", "name": "Stanford University"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174]]]}", "title": "GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources"}