DOE OSTI · 1958760
Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows
Abstract
Lightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2022 lightning talks, which cover five broad topics: data integrity of scientific workflows; a machine learning-based recommendation system; a Python toolkit for running dynamic ensembles of simulations; a cross-platform, high-performance computing utility for processing shell commands; and a meta(data) framework for reproducing hybrid workflows.
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Abhinit, Ishan, Adams, Emily K., Alam, Khairul, Chase, Brian, Deelman, Ewa, Gorenstein, Lev, Hudson, Stephen, Islam, Tanzima, Larson, Jeffrey, Lentner, Geoffrey, Mandal, Anirban, Navarro, John-Luke, Nicolae, Bogdan, Pouchard, Line, Ross, Rob, Roy, Banani, Rynge, Mats, Serebrenik, Alexander, Vahi, Karan, Wild, Stefan, Xin, Yufeng, da Silva, Rafael Ferreira, Filgueira, Rosa. 2022-01-01. Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows. https://doi.org/10.1109/works56498.2022.00016
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