FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows
Published in Phil. Trans. R. Soc. A, 2022
Recommended citation: Sonia Natalie Mitchell, Andrew Lahiff, Nathan Cummings, Jonathan Hollocombe, Bram Boskamp, Dennis Reddyhoff, Ryan Field, Kristian Zarebski, Antony Wilson, Martin Burke, Blair Archibald, Paul Bessell, Richard Blackwell, Lisa A Boden, Alys Brett, Sam Brett, Ruth Dundas, Jessica Enright, Alejandra N. Gonzalez-Beltran, Claire Harris, Ian Hinder, Christopher David Hughes, Martin Knight, Vino Mano, Ciaran McMonagle, Dominic Mellor, Sibylle Mohr, Glenn Marion, Louise Matthews, Iain J. McKendrick, Christopher Mark Pooley, Thibaud Porphyre, Aaron Reeves, Edward Townsend, Robert Turner, Jeremy Walton, Richard Reeve. "FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows" https://arxiv.org/abs/2110.07117 https://doi.org/10.1098/rsta.2021.0300
This peer-reviewed paper presents the FAIR Data Pipeline, which is a provenance-driven data management for traceable scientific workflows.
The associated website is: https://www.fairdatapipeline.org/
All the code is open source and available at https://github.com/fairDataPipeline/.
We published a pre-print for which you can see the details here.