2 items tagged with 'FAIR Principles'.
Abstract (Expand)
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.
Authors: Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober
Date Published: 2020
Publication Type: Journal
DOI: 10.1162/dint_a_00033
Citation: Data Intellegence 2(1-2):108-121
Created: 1st Dec 2021 at 21:43, Last updated: 16th Jan 2023 at 13:34
invited presentation at https://researchsoft.github.io/FAIReScience/, FAIReScience 2021 online workshop virtually co-located with the 17th IEEE International Conference on eScience (eScience 2021)
20th Sept 2021
Creator: Carole Goble
Submitter: Carole Goble
Created: 1st Dec 2021 at 21:01, Last updated: 1st Dec 2021 at 21:23