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Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities

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scientific-workflows.pdf (491.7Kb)
Date
2017
Dewey
Programmation, logiciels, organisation des données
Sujet
Reproducibility; Scientific Workflows; Packaging environments; Provenance
Journal issue
Future Generation Computer Systems
Volume
75
Publication date
2017
Article pages
284-298
DOI
http://dx.doi.org/10.1016/j.future.2017.01.012
URI
https://basepub.dauphine.fr/handle/123456789/20771
Collections
  • LAMSADE : Publications
Metadata
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Author
Cohen-Boulakia, Sarah
Belhajjame, Khalid
Collin, Olivier
Chopard, Jérôme
Froidevaux, Christine
Gaignard, Alban
Type
Article accepté pour publication ou publié
Abstract (EN)
With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance.The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.

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