Skip to end of metadata
Go to start of metadata

Integrated, collaborative modeling is increasingly recognized as key long-term goal for USGS and other science practitioners. It is also a key purpose and outcome of the FAIR data principles. Yet abundant challenges exist to making data and scientific models available and interoperable, including (1) semantics, (2) data storage and services, (3) the use of multiple programming languages and modeling paradigms (both deductive and inductive approaches, including machine learning), (4) uncertainty propagation, and (5) ensuring data and model reuse at appropriate spatiotemporal scales. Modeling protocols and frameworks, like those of the Community Surface Dynamics Modeling System (CSDMS) and Artificial Intelligence for Ecosystem Services (ARIES)/Integrated Modelling Partnership, provide examples of practical methods to overcome these integration challenges. In this session, presenters will address theories and methods to overcome the aforementioned challenges, potential applications, along with potential lessons learned that could inform integrated modeling for USGS and beyond.

  • Introduction - Ken Bagstad, USGS
  • Cloud-Hosted Real-Time Data Services for the Geosciences (CHORDS): Bringing new observations to scientists in real-time - Mike Daniels, NCAR
  • The Scientific Variable Ontology and How it Supports Cross-Domain Interoperability - Scott Peckham, University of Colorado, Boulder
  • Integrated modeling: the CSDMS Approach - Mark Piper, University of Colorado, Boulder
  • Artificial Intelligence for Ecosystem Services (ARIES): An application of the FAIR Principles - Ken Bagstad, USGS

Science support framework category: Applications

Authors: Ken Bagstad ( – Geosciences & Environmental Change Science Center; Mark Piper ( - University of Colorado

Notes Document:

1 Comment

  1. Another related group that could be invited beyond CSDMS & ARIES/Integrated Modelling Partnership: