FAIR Computational Workflows

Research output: Contribution to journalArticle

  • External authors:
  • Sarah Cohen-Boulakia
  • Daniel Garijo
  • Yolanda Gil
  • Michael R. Crusoe
  • Kristian Peters
  • Daniel Schober


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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)108–121
Number of pages14
JournalData Intelligence
Issue number1
Publication statusPublished - 1 Nov 2019

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