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Published July 6, 2019 | Version v2
Journal article Open

FAIR Computational Workflows

Creators

  • 1. School of Computer Science, The University of Manchester
  • 2. Laboratoire de Recherche en Informatique, Université Paris-Sud
  • 3. School of Computer Science, The University of Manchester; Common Workflow Language project, Software Freedom Conservancy
  • 4. Information Science Institute, USC Viterbi
  • 5. Common Workflow Language project, , Software Freedom Conservancy
  • 6. Leibniz Institute of Plant Biochemistry (IPB Halle), Department of Biochemistry of Plant Interactions

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 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. 

Notes

Accepted for Data Intelligence special issue: FAIR best practices 2019. Carole Goble acknowledges funding by BioExcel2 (H2020 823830), IBISBA1.0 (H2020 730976) and EOSCLife (H2020 824087) . Daniel Schober's work was financed by Phenomenal (H2020 654241) at the initiation-phase of this effort, current work in kind contribution. Kristian Peters is funded by the German Network for Bioinformatics Infrastructure (de.NBI) and acknowledges BMBF funding under grant number 031L0107. Stian Soiland-Reyes is funded by BioExcel2 (H2020 823830). Daniel Garijo, Yolanda Gil, gratefully acknowledge support from DARPA award W911NF-18-1-0027, NIH award 1R01AG059874-01, and NSF award ICER-1740683.

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Funding

High resolution mapping of the genetic risk for disease in the aging brain 1R01AG059874-01
National Institutes of Health
BioExcel – Centre of Excellence for Biomolecular Research 675728
European Commission
BioExcel-2 – BioExcel Centre of Excellence for ComputationalBiomolecular Research 823830
European Commission
EOSC-Life – Providing an open collaborative space for digital biology in Europe 824087
European Commission
IBISBA 1.0 – Industrial Biotechnology Innovation and Synthetic Biology Accelerator 730976
European Commission
PhenoMeNal – PhenoMeNal: A comprehensive and standardised e-infrastructure for analysing medical metabolic phenotype data 654241
European Commission
ELIXIR-EXCELERATE – ELIXIR-EXCELERATE: Fast-track ELIXIR implementation and drive early user exploitation across the life-sciences. 676559
European Commission