<p>Reproducible data engineering pipelines using common workflow language and apache airflow</p>

Michael Bouzinier; Mahmood M Shad; Danielle Braun; Mikhail  Polykovskiy; Arunava  Majumdar; Scott Yockel

doi:10.7490/f1000research.1119035.1

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Bouzinier M, Shad MM, Braun D et al. Reproducible data engineering pipelines using common workflow language and apache airflow [version 1; not peer reviewed]. F1000Research 2022, 11:792 (slides) (https://doi.org/10.7490/f1000research.1119035.1)

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Reproducible data engineering pipelines using common workflow language and apache airflow

Michael Bouzinier¹, Mahmood M Shad¹, Danielle Braun², Mikhail Polykovskiy, Arunava Majumdar, Scott Yockel

Published 17 Jul 2022 (https://doi.org/10.7490/f1000research.1119035.1)

Author Affiliations

¹ Harvard University Faculty of Arts and Sciences Research Computing, USA
² Harvard T.H. Chan School of Public Health, Harvard University, USA

Copyright © 2022 Bouzinier M et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 17 Jul 2022

Reproducible data engineering pipelines using common workflow language and apache airflow
[version 1; not peer reviewed]

Michael Bouzinier¹, Mahmood M Shad¹, Danielle Braun², Mikhail Polykovskiy, Arunava Majumdar, Scott Yockel

Author Affiliations

¹ Harvard University Faculty of Arts and Sciences Research Computing, USA
² Harvard T.H. Chan School of Public Health, Harvard University, USA

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Intelligent Systems for Molecular Biology (ISMB) 2022

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Reproducible research, workflows, Airflow, CWL, Population health

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