FAIR Computational Workflows

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...

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Published inData 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
LanguageEnglish
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
Online AccessGet full text

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Abstract 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.
AbstractList 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.
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.
Author Schober, Daniel
Soiland-Reyes, Stian
Gil, Yolanda
Crusoe, Michael R.
Goble, Carole
Cohen-Boulakia, Sarah
Garijo, Daniel
Peters, Kristian
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  organization: Department of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
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  surname: Cohen-Boulakia
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  organization: Laboratoire de Recherche en Informatique, CNRS, Université Paris-Saclay, Batiment 650, Université Paris-Sud, 91405 ORSAY Cedex, France
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  fullname: Soiland-Reyes, Stian
  organization: Common Workflow Language project, Software Freedom Conservancy, Inc. 137 Montague St STE 380, NY 11201-3548, USA
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  organization: Leibniz Institute of Plant Biochemistry (IPB Halle), Department of Biochemistry of Plant Interactions, Weinberg 3, 06120 Halle (Saale), Germany
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  givenname: Daniel
  surname: Schober
  fullname: Schober, Daniel
  organization: Leibniz Institute of Plant Biochemistry (IPB Halle), Department of Biochemistry of Plant Interactions, Weinberg 3, 06120 Halle (Saale), Germany
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R1.3 Computational workflow Reproducibility Software FAIR data Provenance
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Workflows centrés sur les données
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FAIR data
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Snippet Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and...
Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and...
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SubjectTerms Automation
Biochemistry
Bioinformatics
Computational workflow
Computer Science
Data collection
Data processing
FAIR data
Infrastructure
Metadata
Prediction models
Principles
Provenance
Quality assessment
Reproducibility
Software
Title FAIR Computational Workflows
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