IN-CORE: Computational Modeling Platform for Community Resilience
Creators: name: Panchani, Rashmil
name: Navarro, Christopher
name: Lee, Jong
name: Wang, Chen
name: Kooper, Rob
name: Kim, Yong Wook
name: Mohanarajan, Vismayak
name: Yang, Ya-Lan
name: Gatzke, Lisa
name: Watkins, Lisa
Publisher: name: Zenodo
Year: 2023
Subjects: subject: IN-CORE
subject: Community Resilience
subject: Computing Platform
subject: Hazard Risk Assessment
subject: Scientific Workflow System
subject: Container Technology
subject: Kubernetes
subject: Architecture
subject: FOS: Civil engineering
subject: Cyberinfrastructure
subject: Package Development
subject: Web Tools
subject: Multi-User Software Platform

description: Community resilience research is critical for anticipating, preventing, and mitigating natural and anthropic disaster impacts. In the digital age, it requires robust and flexible cyberinfrastructure to support research and decision-making processes. To address this need, the National Center for Supercomputing Applications (NCSA) developed the Interdependent Networked Community Resilience Modeling Environment (IN-CORE) as part of the National Institute of Standards and Technology (NIST)-funded Center of Excellence for Risk-Based Community Resilience Planning (CoE), headquartered at Colorado State University. IN-CORE provides a platform to implement the measurement science as analyses to help assess community resilience to different hazards. The architecture of IN-CORE consists of 1) Python packages, eg. pyincore, 2) RESTful web services, 3) light-weight web tools, and 4) multi-user Jupyter Lab, that talk to each other using an authentication technology, Keycloak. Pyincore, one of the core components of the IN-CORE infrastructure is a high-performance Python package, consisting of two primary components: 1) a set of service classes to interact with the IN-CORE web services described below, and 2) IN-CORE analyses. The pyincore package allows users to apply various hazards to infrastructure in selected areas, propagating the effect of physical infrastructure damage and loss of functionality to social and economic impacts. It utilizes the various micro-services that enable the user to create, visualize, and use the existing datasets to run different scientific algorithms. This set of micro-services is collectively referred to as IN-CORE web services, and is comprised of 1) Data Service, responsible for handling datasets in various formats, 2) DFR3 Service (Damage, Functionality, Repair, Restoration, and Recovery functions), which describes expected damage, functionality, etc, 3) Hazard Service, which enables the user to create different hazards through the pyincore package, 4) Space Service is used for resource access management and keeping track of the users in the system, and 5) Semantic Service, the newest service which provides a semantic description of the datasets and their fields. Also, by extension, the management team can perform rigorous checks on the data ingested into the system to keep datasets consistent. These two, pyincore and IN-CORE Web Services form the basis of the IN-CORE ecosystem. To augment its capabilities, the IN-CORE Web Tools is a set of React/JS web applications that help users to navigate through the datasets and obtain the necessary information to create and run analyses. Ensuring accessibility and flexibility for users, the IN-CORE Lab, a multi-user Jupyter Hub, facilitates cloud-based experimentation. With all the necessary pre-installed dependencies, users can run example Jupyter notebooks and develop their analyses seamlessly from anywhere and any machine, enhancing the platform's user-friendliness and convenience. These crucial pieces that make up the entire infrastructure of IN-CORE are containerized using Docker and deployed on the NCSA Radiant Cloud system. These containers are managed and orchestrated using Kubernetes to ensure a smooth user experience and allow the system to scale. In conclusion, the IN-CORE ecosystem represents a significant advancement in the field of community resilience assessment, bringing together cutting-edge technologies and methodologies to anticipate and mitigate the impacts of disasters.