A Scalable and Secure Cyberinfrastructure for the Repeatability of Ecological Research

Pl: Michael Dietz, Earth & Environment, CAS
Co-Pl: Abraham Matta, Computer Science, CAS

The goal of this project is to build a scalable, cloud-based system for submitting, generating, archiving, and disseminating multi-model ecological forecasts. Beyond advancing ecological research and socially-useful forecasts, this system will contribute to the more general development of a scalable and secure cyberinfrastructure for automated, repeatable scientific analyses applied to real-time data. As the Earth rapidly moves outside the envelope of historical environmental conditions, it is increasingly urgent that ecologists be able to provide managers and policymakers with quantitative projections that appropriately account for and quantify sources of forecast uncertainty. This project represents one of the first efforts to make real-time forecasts of ecological processes with fully specified uncertainties using Bayesian statistical modeling and data-driven approaches.

This work is funded by a Research Incubation Award made in January, 2018.