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Interoperability between varied, multidisciplinary data and models based on the FAIR Data Principles, combined with AI, offers a path forward to make scientific modeling faster and more transparent while improving the reuse of existing knowledge. This poster describes how the Artificial Intelligence for Ecosystem Services (ARIES) environmental and Earth sciences modeling platform fosters interoperability between a wide range of scientific data and models while applying AI (machine learning, machine reasoning, and semantics) to fully support both deductive and inductive modeling. Since 2007, the ARIES team has worked to develop (1) semantics that work across diverse scientific disciplines and describe data and model elements with the underlying logic and parsimony to support machine reasoning; and (2) open data and models that can be linked together using (3) open-source software tools. These software tools include a technical ARIES Modeler interface that experienced scientists and modelers can use to link and contribute data and models to a growing knowledge base. Additionally, a web-based ARIES Explorer allows nontechnical stakeholders to run models, visualize data, download model input and output data, and understand model workflow provenance. A concurrent demonstration of the ARIES Modeler and Web Explorer will be provided.

Science Support Framework Category: Applications

Author(s): Ken Bagstad (kjbagstad@usgs.gov) - USGS Geosciences & Environmental Change Science Center; Ferdinando Villa (ferdinando.villa@bc3research.org) - Basque Centre for Climate Change; Stefano Balbi (stefano.balbi@bc3research.org) - Basque Centre for Climate Change


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