Enhancing Reproducibility in Research Through FAIR Digital Objects
Keywords:Reproducibility, FAIR Digital Object, FAIR principles
The FAIR principles were introduced to enhance data reuse by providing guidelines for effective data management practices. In the broader context of research, assets encompass not only data but also artifacts such as code, software, and publications. FAIRifying these artifacts is as essential as FAIRifying data, given the increasing complexity of current AI approaches that make reproducibility extremely challenging. Therefore, the reuse of these artifacts is growing in importance. The concept of FAIR Digital Objects (FDOs) presents a solution to FAIRify these artifacts, treating them as FDOs. NFDI4DataScience is embracing FDOs and proposing an architecture to efficiently manage them.
M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, et al., “The fair guiding principles for scientific data management and stewardship,” Scientific data, vol. 3, no. 1, pp. 1–9, 2016.
M. Barker, N. P. Chue Hong, D. S. Katz, et al., “Introducing the fair principles for research software,” Scientific Data, vol. 9, no. 1, p. 622, 2022.
C. Goble, S. Cohen-Boulakia, S. Soiland-Reyes, et al., “Fair computational workflows,” Data Intelligence, vol. 2, no. 1-2, pp. 108–121, 2020.
K. De Smedt, D. Koureas, and P. Wittenburg, “Fair digital objects for science: From data pieces to actionable knowledge units,” Publications, vol. 8, no. 2, p. 21, 2020.
E. Schultes and P. Wittenburg, “Fair principles and digital objects: Accelerating convergence on a data infrastructure,” in Data Analytics and Management in Data Intensive Domains: 20th International Conference, DAMDID/RCDL 2018, Moscow, Russia, October 9–12, 2018, Revised Selected Papers 20, Springer, 2019, pp. 3–16.
U. Schwardmann, “Digital objects–fair digital objects: Which services are required?” Data Science Journal, vol. 19, no. 1, 2020.
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Copyright (c) 2023 Zeyd Boukhers, Leyla Jael Castro
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Grant numbers NFDI4DataSciene (460234259)