Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows

Ishan Abhinit, Emily K. Adams, Khairul Alam, Brian Chase, Ewa Deelman, Lev Gorenstein, Stephen Hudson, Tanzima Islam, Jeffrey Larson, Geoffrey Lentner, Anirban Mandal, John-Luke Navarro, Bogdan Nicolae, Line Pouchard, Rob Ross, Banani Roy, Mats Rynge, Alexander Serebrenik, Karan Vahi, Stefan Wild, Yufeng Xin, Rafael Ferreira da Silva, Rosa Filgueira

2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS)（2022）

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摘要

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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关键词

scientific workflows,FAIR,high performance computing,data integrity,ensembles,machine learning

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