FAIR_on_frim
ODAM: FAIR grids¶
FAIR grids applied on FRIM dataset¶
Three FAIR grids avery different from each other.
- 1- 5 ★ Data Rating Tool
- From OZONOME, it aims to carry out an evaluation based on the FAIR principles as defined by Willkinson et al(1). The main output is a global rating, indicating the global FAIRness of the dataset. It provides implementations of the FORCE 11 FAIR data principles.
- 2 - FDMM (FAIR Data Maturity Model)(2)
FDMM is a recognized and endorsed working group within RDA (Research Data Alliance). They produce a document that describes a maturity model for FAIR assessment with assessment indicators, priorities and evaluation methods, useful for the normalisation of assessment approaches to enable comparison of their results.
- 3 - SHARC (Sharing Rewards and Credit)(3)
SHARC is a recognized and endorsed working group within RDA (Research Data Alliance). They produce a document that allows assessing FAIRness of projects and related human processes by either external evaluators or the researchers themselves, implying to implement simple FAIRness assessment in various communities and identify procedures and training that must be deployed and adapted to their practices and level of understanding.
Results¶
- 5 ★ Data Rating Tool
- Summary table
https://csiro-enviro-informatics.github.io/5stardata/ - FDMM (FAIR Data Maturity Model)
- https://drive.google.com/file/d/1a520Cbu8bryEeZIPI3h1l6zkaO7MZ39-/view?usp=sharing"
- SHARC (Sharing Rewards and Credit)
- https://drive.google.com/file/d/1uif-jy9QBno_WPnpGL14LFpDzL366tMH/view?usp=sharing
Synthesis of FAIR evaluation grids applied to the Frim dataset
The Fair Data Maturity Model (FDMM) document (A) describes a maturity model for the FAIR assessment with indicators, priorities and assessment methods, which are useful for standardizing assessment approaches in order to allow comparison of their results. Whereas the FAIR SHARC (SHAring Rewards and Credit) (B) document allows the fairness of projects and associated human processes to be assessed, either by external evaluators or by the researchers themselves. Therefore, these grids cannot be compared with each other, but rather complement each other.
Summary table of essential FAIR criteria based on force11.org, applied to the Frim dataset
References¶
- Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018. doi:10.1038/sdata.2016.18
- RDA FAIR Data Maturity Model Working Group (2020). FAIR Data Maturity Model: specification and guidelines. Research Data Alliance. DOI: 10.15497/RDA00045
- David, R, Mabile, L, Specht, A, Stryeck, S, Thomsen, M, Yahia, M, Jonquet, C, Dollé, L, Jacob, D, Bailo, D, Bravo, E, Gachet, S, Gunderman, H, Hollebecq, J-E, Ioannidis, V, Bras, YL, Lerigoleur, E, Cambon-Thomsen, A and The Research Data Alliance – SHAring Reward and Credit (SHARC) Interest Group. 2020. FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles. Data Science Journal, 19: 32, pp. 1–11. DOI: https://doi.org/10.5334/dsj-2020-032