FAIR data pipeline: provenance-driven data management for traceable scientific workflows

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecis...

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Detalles Bibliográficos
Autores principales: Mitchell, Sonia Natalie, Lahiff, Andrew, Cummings, Nathan, Hollocombe, Jonathan, Boskamp, Bram, Field, Ryan, Reddyhoff, Dennis, Zarebski, Kristian, Wilson, Antony, Viola, Bruno, Burke, Martin, Archibald, Blair, Bessell, Paul, Blackwell, Richard, Boden, Lisa A., Brett, Alys, Brett, Sam, Dundas, Ruth, Enright, Jessica, Gonzalez-Beltran, Alejandra N., Harris, Claire, Hinder, Ian, David Hughes, Christopher, Knight, Martin, Mano, Vino, McMonagle, Ciaran, Mellor, Dominic, Mohr, Sibylle, Marion, Glenn, Matthews, Louise, McKendrick, Iain J., Mark Pooley, Christopher, Porphyre, Thibaud, Reeves, Aaron, Townsend, Edward, Turner, Robert, Walton, Jeremy, Reeve, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376726/
https://www.ncbi.nlm.nih.gov/pubmed/35965468
http://dx.doi.org/10.1098/rsta.2021.0300

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9376726/
https://www.ncbi.nlm.nih.gov/pubmed/35965468
http://dx.doi.org/10.1098/rsta.2021.0300

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