Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities
作者： Sarah Cohen-Boulakia , Khalid Belhajjame , Olivier Collin , Jérôme Chopard , Christine Froidevaux
DOI: 10.1016/J.FUTURE.2017.01.012
关键词: Computer science 、 Domain (software engineering) 、 Context (language use) 、 Set (psychology) 、 Data science 、 Reproducibility 、 Workflow 、 Use case
摘要: With the development of new experimental technologies, biologists are faced with an avalanche data to be computationally analyzed for scientific advancements and discoveries emerge. Faced complexity analysis pipelines, large number computational tools, enormous amount manage, there is compelling evidence that many if not most will stand test time: increasing reproducibility computed results paramount importance. The objective we set out in this paper place workflows context reproducibility. To do so, define several kinds repro-ducibility can reached when used perform experiments. We characterize criteria need catered by reproducibility-friendly workflow systems, use such representative widely systems companion tools within a framework. also discuss remaining challenges posed reproducible life sciences. Our study was guided three cases from science domain involving silico
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