{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "David Olson", "hasEmail": "mailto:olson.david@epa.gov"}, "description": "Underlying data associated with figures in publication. Portions of this dataset are inaccessible because: Data is now available for public access. They can be accessed through the following means: Data available through Data.gov and EDG. Format: Excel spreadsheet. \n\nThis dataset is associated with the following publication:\nOlson, D., T. Riedel, J. Offenberg, M. Lewandowski, R. Long, and T. Kleindienst. Quantifying wintertime O3 and NOx formation with relevance vector machines.   ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 259: 118538, (2021).", "distribution": [{"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1520921/ScienceHub%20entry%20for%20RVM%20Utah%20%28Olson%20et%20al.%2C%202021%29.xlsx", "mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "title": "ScienceHub entry for RVM Utah (Olson et al., 2021).xlsx"}], "identifier": "https://doi.org/10.23719/1520921", "keyword": ["Ozone", "Secondary Organic Aerosol", "air quality", "fine particulate matter (PM2.5)", "machine learning"], "license": "https://pasteur.epa.gov/license/sciencehub-license.html", "modified": "2021-06-15", "programCode": ["020:094"], "publisher": {"name": "U.S. EPA Office of Research and Development (ORD)", "subOrganizationOf": {"name": "U.S. Environmental Protection Agency", "subOrganizationOf": {"name": "U.S. Government"}}}, "references": ["https://doi.org/10.1016/j.atmosenv.2021.118538"], "rights": null, "title": "Quantifying wintertime O3 and NOx formation with relevance vector machines"}