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- Carole Goble
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
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- Sarah Cohen-Boulakia
- Laboratoire de Recherche en Informatique, CNRS, Université Paris-Saclay, Batiment 650, Université Paris-Sud, 91405 ORSAY Cedex, France
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- Stian Soiland-Reyes
- Department of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
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- Daniel Garijo
- Information Sciences Institute, University of Southern California, Marina Del Rey CA 90292, USA
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- Yolanda Gil
- Information Sciences Institute, University of Southern California, Marina Del Rey CA 90292, USA
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- Michael R. Crusoe
- Common Workflow Language project, Software Freedom Conservancy, Inc. 137 Montague St STE 380, NY 11201-3548, USA
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- Kristian Peters
- Leibniz Institute of Plant Biochemistry (IPB Halle), Department of Biochemistry of Plant Interactions, Weinberg 3, 06120 Halle (Saale), Germany
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- Daniel Schober
- Leibniz Institute of Plant Biochemistry (IPB Halle), Department of Biochemistry of Plant Interactions, Weinberg 3, 06120 Halle (Saale), Germany
抄録
<jats:p> 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. </jats:p>
収録刊行物
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- Data Intelligence
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Data Intelligence 2 (1-2), 108-121, 2020-01
MIT Press - Journals