FAIR Computational Workflows

• Published in: Data intelligence Vol. 2; no. 1-2; pp. 108 - 121
• Main Authors: Goble, Carole, Cohen-Boulakia, Sarah, Soiland-Reyes, Stian, Garijo, Daniel, Gil, Yolanda, Crusoe, Michael R., Peters, Kristian, Schober, Daniel
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.01.2020; MIT Press Journals, The; Paramus NJ: Rinton Press
• Subjects: Automation; Biochemistry; Bioinformatics; Computational workflow; Computer Science; Data collection; Data processing; FAIR data; Infrastructure; Metadata; Prediction models; Principles; Provenance; Quality assessment; Reproducibility; Software; Provenance; FAIR; R1.3 Computational workflow Reproducibility Software FAIR data Provenance; Reproducibility; Workflows centrés sur les données; Software; R1.3 Computational workflow; FAIR data
• ISSN: 2641-435X; 2577-610X; 2641-435X; 2577-610X
• DOI: 10.1162/dint_a_00033

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.