FAIR Computational Workflows

Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober

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


  • FAIR
  • workflow
  • scientific workflow
  • reproducibility

Research Beacons, Institutes and Platforms

  • Institute for Data Science and AI


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  • Workflows Community Summit: Bringing the Scientific Workflows Community Together.

    Silva, R. F. D., Casanova, H., Chard, K., Laney, D., Ahn, D., Jha, S., Goble, C. A., Ramakrishnan, L., Peterson, L., Enders, B., Thain, D., Altintas, I., Babuji, Y. N., Badia, R. M., Bonazzi, V., Coleman, T., Crusoe, M. R., Deelman, E., Natale, F. D., Tommaso, P. D., & 25 othersFahringer, T., Filgueira, R., Fursin, G., Ganose, A., Gruning, B., Katz, D. S., Kuchar, O., Kupresanin, A., Ludäscher, B., Maheshwari, K., Mattoso, M., Mehta, K., Munson, T., Ozik, J., Peterka, T., Pottier, L., Randles, T., Soiland-Reyes, S., Tovar, B., Turilli, M., Uram, T. D., Vahi, K., Wilde, M., Wolf, M. & Wozniak, J. M., 2021, Zenodo. 25 p.

    Research output: Book/ReportOther report

    Open Access
  • The FAIR Guiding Principles for scientific data management and stewardship

    Wilkinson, M. D., Dumontier, M., Aalbersberg, IJ. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., & 33 othersGray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., 't Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S. A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., Van Der Lei, J., Van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J. & Mons, B., 2016, In: Scientific Data. 3, 160018.

    Research output: Contribution to journalComment/debatepeer-review

    Open Access
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