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Enabling collaborative numerical modeling in earth sciences using knowledge infrastructure

Bandaragoda, C, Castronova, A, Istanbulluoglu, E, Strauch, R, Phuong, J, Adams, J, Gasparini, N, Barnhart, K, Hutton, E, Hobley, Daniel ORCID:, Lyons, N, Tucker, G, Tarboton, D, Isaszak, R and Wang, S 2019. Enabling collaborative numerical modeling in earth sciences using knowledge infrastructure. Environmental Modelling and Software 120 , 104424. 10.1016/j.envsoft.2019.03.020

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Knowledge Infrastructure is an intellectual framework for creating, sharing, and distributing knowledge. In this paper, we use Knowledge Infrastructure to address common barriers to entry to numerical modeling in Earth sciences: computational modeling education, replicating published model results, and reusing published models to extend research. We outline six critical functional requirements: 1) workflows designed for new users; 2) a community-supported collaborative web platform; 3) distributed data storage; 4) a software environment; 5) a personalized cloud-based high-performance computing platform; and 6) a standardized open source modeling framework. Our methods meet these functional requirements by providing three interactive computational narratives for hands-on, problem-based research demonstrating how to use Landlab on HydroShare. Landlab is an open-source toolkit for building, coupling, and exploring two-dimensional numerical models. HydroShare is an online collaborative environment for the sharing of data and models. We describe the methods we are using to accelerate knowledge development by providing a suite of modular and interoperable process components that allows students, domain experts, collaborators, researchers, and sponsors to learn by exploring shared data and modeling resources. The system is designed to support uses on the continuum from fully-developed modeling applications to prototyping research software tools.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Environmental Sciences
Publisher: Elsevier
ISSN: 1364-8152
Date of First Compliant Deposit: 12 April 2019
Date of Acceptance: 26 March 2019
Last Modified: 28 Oct 2022 00:28

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