Run your scientific analyses that model the impact of natural hazards on a community and the resilience of those communities.

The National Institute of Standards and Technology (NIST) funded the multi-university five-year Center of Excellence for Risk-Based Community Resilience Planning (CoE), headquartered at Colorado State University, to develop the measurement science to support community resilience assessment. Measurement science is implemented on a platform called Interdependent Networked Community Resilience Modeling Environment (IN-CORE). On IN-CORE, users can run scientific analyses that model the impact of natural hazards and resiliency against the impact on communities. The IN-CORE platform is built on a Kubernetes cluster with Docker container technology.

What's new in IN-CORE v5.5.1?
Current Version: v5.5.1

Web Services

v1.27.1
Change logGitHub

Web Tools

v1.13.1
Change logGitHub

IN-CORE Lab

v1.9.1
Change logGitHub

Getting Started

Here are two simple steps to quickly get you up and running with IN-CORE:

  1. Sign up for an IN-CORE account
  2. Install pyincore, a python package that contains service classes to connect with IN-CORE web services and functionalities for IN-CORE analyses.
    conda config --add channels conda-forge
    conda install -c in-core pyincore

Learn IN-CORE

Gain a comprehensive understanding of IN-CORE by reviewing the step-by-step manual. To delve deeper into advanced IN-CORE topics, check out our detailed tutorials.


Get Help

Got questions or need assistance? There are three ways to reach out for help review our: FAQs for the most common questions, join the IN-CORE Slack channel, or email our dev team.