Abstract

Model repositories are key resources for scientists in terms of model discovery and reuse, but do not focus on important tasks such as model comparison and composition. Model repositories do not typically capture important comparative metadata to describe assumptions and model variables that enable a scientist to discern which models would be better for their purposes. Furthermore, once a scientist selects a model from a repository it takes significant effort to understand and use the model. Our goal is to develop model repositories with machine-actionable model metadata that can be used to provide intelligent assistance to scientists in model selection and reuse. We are extending the OntoSoft semantic software metadata registry (http://www. ontosoft. org/) to include machine-readable metadata. This work includes: 1) exposing model variables and their relationships; 2) exposing model processes and how they …

Date

2018

Authors

Daniel Garijo, Yolanda Gil, Varun Ratnakar

Journal

AGU Fall Meeting Abstracts

Volume

2018

Pages

IN13A-03