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dc.contributor.authorCohen-Boulakia, Sarah
HAL ID: 15627
dc.contributor.authorBelhajjame, Khalid
dc.contributor.authorCollin, Olivier
HAL ID: 1483
ORCID: 0000-0002-8959-8402
dc.contributor.authorChopard, Jérôme
dc.contributor.authorFroidevaux, Christine
HAL ID: 15597
dc.contributor.authorGaignard, Alban
HAL ID: 1448
ORCID: 0000-0002-3597-8557
dc.date.accessioned2020-05-25T10:10:16Z
dc.date.available2020-05-25T10:10:16Z
dc.date.issued2017
dc.identifier.issn0167-739X
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20771
dc.language.isoenen
dc.subjectReproducibility
dc.subjectScientific Workflows
dc.subjectPackaging environments
dc.subjectProvenance
dc.subject.ddc005en
dc.titleScientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWith 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.
dc.relation.isversionofjnlnameFuture Generation Computer Systems
dc.relation.isversionofjnlvol75
dc.relation.isversionofjnldate2017
dc.relation.isversionofjnlpages284-298
dc.relation.isversionofdoi10.1016/j.future.2017.01.012
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2020-12-17T09:29:08Z


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