{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "Sudhakar Takkellapati", "hasEmail": "mailto:takkellapati.sudhakar@epa.gov"}, "description": "The emission data used is based on this publication. \n\nThis dataset is associated with the following publication:\nTakkellapati, S., and M.A. Gonzalez. Application of read-across methods as a framework for the estimation of emissions from chemical processes.   Clean Technologies and Recycling. AIMS Press, Springfield, MO, USA, 3(4): 283-300, (2023).", "distribution": [{"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1529986/Meyer_ImprovingReliabilityOfChemicalManufactLCI_2020.pdf", "mediaType": "application/pdf", "title": "Meyer_ImprovingReliabilityOfChemicalManufactLCI_2020.pdf"}], "identifier": "https://doi.org/10.23719/1529986", "keyword": ["Environmental Releases", "Read-across", "climate change", "emissions", "machine learning"], "license": "https://pasteur.epa.gov/license/sciencehub-license.html", "modified": "2024-01-11", "programCode": ["020:000"], "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/10.3934/ctr.2023018"], "rights": null, "title": "Read-across application for emissions estimation"}