期刊
DATA INTELLIGENCE
卷 2, 期 1-2, 页码 108-121出版社
MIT PRESS
DOI: 10.1162/dint_a_00033
关键词
Computational workflow; Reproducibility; Software; FAIR data; Provenance
资金
- BioExcel2 [H2020 823830]
- IBISBA1.0 [H2020 730976]
- EOSCLife [H2020 824087]
- German Network for Bioinformatics Infrastructure (de.NBI)
- BMBF [031L0107]
- DARPA [W911NF-18-1-0027]
- NIH [1R01AG059874-01]
- NSF [ICER-1740683]
- [H2020 654241]
- BBSRC [BB/L005050/1] Funding Source: UKRI
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.
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