FAIR Computational Workflows
FAIR Computational Workflows
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DOI：
10.1162/dint_a_00033
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发表时间：
2020-12-01
影响因子：
3.9
通讯作者：
Schober, Daniel
中科院分区：
文献类型：
--
作者：
Goble, Carole;Cohen-Boulakia, Sarah;Schober, Daniel
计算工作流描述了用于数据收集、数据准备、分析、预测建模和模拟的复杂多步骤方法，这些方法可以产生新的数据产品。它们可以通过以下方式对公平研究数据原则作出固有的贡献：根据既定的元数据处理数据;在数据处理过程中自己创建元数据;跟踪和记录数据来源。这些属性有助于数据质量评估，并有助于辅助数据的使用。此外，工作流本身就是数字对象。本文认为，公平的原则，工作流需要解决其特定的性质，在其组成的可执行的软件步骤，它们的出处，和它们的发展。
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. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.