Collaborative Research: EarthCube Data Capabilities: A data-driven modeling infrastructure to support research and education in volcanology, geochemistry and petrology

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Research and education in volcanology, geochemistry, and petrology rely on data-driven modeling tools. Modeling helps us to understand the conditions under which rocks form, how magmas are transported from the Earth's interior to erupt as lavas at the surface, and how temperature, pressure, and composition affect the style and dynamics of volcanic eruptions. Observational and experimental data are essential to calibrating models and to evaluating the veracity of model outcomes. This project focuses on integrating data resources at EarthChem (http://www.earthchem.org) with the modeling capabilities of the ENKI software framework (http://enki-portal.org). The objective is to fully integrate these two resources and to make them available to the scientific community as cloud-accessible tools that support both research and learning. The integrated tool will encourage community usage of data-driven modeling tools and entrain broader participation of early career researchers in these fields by conducting workshops that expand and engage the ENKI and EarthChem user base. Another goal is to develop workflows that help to publish replicable, reproducible modeling and data-driven research products. All software produced in this project will be open source.This collaborative project has the principal objective of integrating data resources available at EarthChem with thermodynamic and geodynamic modeling capabilities found in the ENKI software framework. The goal is to develop an open-source, container-based and cloud-enabled, data-driven modeling platform to support research and education for the volcanology, geochemistry, and petrology (VGP) community. An equally important objective is to encourage usage of data-driven modeling tools by conducting workshops that expand and engage the ENKI and EarthChem user base and encourage participation of early career researchers in the VGP community. The data analysis and modeling frameworks will support research workflows that facilitate dissemination and publication of results in a manner that is both replicable and reproducible. The project addresses an important and established need of the VGP research community. Previous to the development of the ENKI modeling framework, modeling tools in VGP were principally app-based, operating system-specific, and largely dependent on a small number of researchers for modification and updates. ENKI enabled a paradigm shift in the approach to model-driven research in VGP by creating an infrastructure based on Python frameworks that establish uniform access to underlying tools, streamline the process of model calibration and maintenance, and facilitate platform-independent access by the use of Jupyter notebooks and servers that are accessed as cloud-based resources with a web browser. A deficiency of the ENKI modeling environment is the absence of persistent and well-maintained data resources that directly support model calibration, that constrain parameters important for model usage, and that provide means of evaluating model predictions. The EarthChem data systems contain data that provide these constraints. Integration of EarthChem and ENKI will deliver a robust modeling ecosystem that supports research and education in VGP, and encourages standards in model usage, modeling workflows, and model publication that better enable replicable and reproducible science. Community engagement is a crucial component of the project. Three user workshops will be conducted in a virtual or in person format with the aim of engaging early career VGP researchers in utilizing the EarthChem-ENKI platform. All software produced in this project will be open source. This work is funded by the Directorate for Geosciences and the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.