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 WildYufeng Xin, Rafael Ferreira da Silva, Rosa Filgueira*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publication2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS)
PublisherIEEE
Pages84-92
Number of pages9
ISBN (Electronic)9781665451918
ISBN (Print)9781665451925
DOIs
Publication statusPublished - 13 Nov 2022
Event17th Workshop on Workflows in Support of Large-Scale Science: held in conjunction with Super Computing 2022 (SC22) - Dallas, TX, USA , Dallas, United States
Duration: 14 Nov 202214 Nov 2022
Conference number: 17
https://works-workshop.org/

Workshop

Workshop17th Workshop on Workflows in Support of Large-Scale Science
Abbreviated titleWORKS
Country/TerritoryUnited States
CityDallas
Period14/11/2214/11/22
Internet address

Keywords

  • Scientific workflows
  • FAIR principles
  • High performance computing
  • Data integrity
  • Ensembles
  • Machine learning

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