VICTOR, the Volcanology Infrastructure for Computational Tools and Resources, provides a novel cyberinfrastructure serving the volcanology community. The volcanology community is transforming into a computer-savvy, data-driven, quantitative discipline that requires a matching cyberinfrastructure. Specifically, VICTOR provides a platform for executing numerical simulations of volcanic processes, including lava flows, ash, tephra dispersal, and pyroclastic density currents. The central purpose of VICTOR is to catalyze the volcanology community to advance model quality and access and promote model literacy and overall collaboration.
The VICTOR team puts special emphasis on education and training. We take a multi-faceted approach that combines: (1) inclusion and integration of community software codes, (2) in-person workshops, (3) introductory and specialized webinars, (4) educator training and teaching modules for undergraduate and graduate level classes, and (5) establishment of a community governance structure and effective communication channels.
VICTOR is based on a JupyterHub platform, and access is through a central web portal. All components are based in the cloud, to allow for demand-based resource management, workflow portability, and reproducibility, and to offer access to high-performance computing to a broader community. The project develops computational workflows that use new capabilities and libraries of models to simplify model verification, validation, and benchmarking and streamline access to required external datasets such as topography and environmental conditions using public Application Programming Interfaces (APIs). Workflows utilize modern computing tools such as Jupyter Notebooks, minimizing the time-intensive steps of locating, installing, running, and testing models. Workflows will enable standardization of model inputs and outputs, facilitating studies of linked- and multi-hazard scenarios. The reproducibility and reliability of the modeling process are enhanced through capabilities to save, re-run, edit, and test workflows. Ultimately, the combination of open-access models, data science tools, and the provisioned low-barrier access to computing resources aims to increase usability by the community and accelerate the transition to a culture of open science.