FAIR Computational Workflows
收藏科学数据银行2020-10-17 更新2026-04-23 收录
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资源简介：
Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. Figure 1 shows a workflow for detecting variants in genome sequences. The workflow is specified in the Common Workflow Language, displayed using the CWL Viewer. Any CWL compliant WfMS execution engine should be able to execute this standardized workflow description and obtain the same results independent of their underlying infrastructure, for instance using Toil executed on SLURM, or REANA on Kubernetes.
提供机构：
Software Freedom Conservancy, Inc.; Université Paris-Saclay; The University of Manchester; Leibniz Institute of Plant Biochemistry; University of Southern California
创建时间：
2020-10-17
