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FAIR Computational Workflows


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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:contributor>Carole Goble</dc:contributor>
  <dc:contributor>Stian Soiland-Reyes</dc:contributor>
  <dc:contributor>Daniel Garijo</dc:contributor>
  <dc:contributor>Yolanda Gil</dc:contributor>
  <dc:contributor>Michael R. Crusoe</dc:contributor>
  <dc:contributor>Kristian Peters</dc:contributor>
  <dc:contributor>Daniel Schober</dc:contributor>
  <dc:creator>Sarah COHEN-BOULAKIA</dc:creator>
  <dc:description>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 is a presentation of the paper FAIR Computational Workflows, published in Data Intelligence. The 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.

Presented at ECCB 2020 Workshop on FAIR Computational Workflows.</dc:description>
  <dc:title>FAIR Computational Workflows</dc:title>
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