Which FAIR are you?
A Detailed Comparison of Existing FAIR Metrics in the Context of Research Data Management
DOI:
https://doi.org/10.52825/cordi.v1i.401Keywords:
FAIR, FAIR principles, FAIR metrics, FDM, RDM, Harmonization of RDM, Research Data ManagementAbstract
In data management the high-level FAIR principles are interpreted and implemented in various FAIR metrics. While this specific interpretation is intended, it leads to the situation of several metrics with different evaluation results for the same digital object. This work conducts an organizational-formal comparison, showing up elements like categories of importance in the considered metrics, as well as a content-wise comparison of selected metrics how their differ in their interpretation. The results give orientation especially to everyone in science aiming to find the right metric to make their data FAIR.
Downloads
References
M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, et al., “The FAIR Guiding Principles for scientific data management and stewardship,” Sci Data, vol. 3, no. 160018, Mar. 2016. DOI: 10.1038/sdata.2016.18.
S. Bechhofer, D. De Roure, M. Gamble, C. Goble, and I. Buchan, “Research Objects: Towards Exchange and Reuse of Digital Knowledge,” Nature Precedings, Jul. 2010. DOI: 10.1038/npre.2010.4626.1.
A. Jacobsen, R. de Miranda Azevedo, N. Juty, et al., “FAIR Principles: Interpretations and Implementation Considerations,” Data Intelligence, vol. 2, no. 1-2, pp. 10–29, Jan. 2020. DOI: 10.1162/dint_r_00024.
A. Devaraju and R. Huber, “An automated solution for measuring the progress toward fair research data,” Patterns, vol. 2, no. 11, Nov. 2021. DOI: 10.1016/j.patter.2021.100370.
M. D. Wilkinson, S.-A. Sansone, G. Marjan, J. Nordling, R. Dennis, and D. Hecker, “FAIR Assessment Tools: Towards an ”Apples to Apples” Comparisons,” Zenodo, Dec. 2022. DOI: 10.5281/zenodo.7463421.
K. Peters-von Gehlen, H. H¨ock, A. Fast, D. Heydebreck, A. Lammert, and H. Thiemann, “Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools,” Data Science Journal, vol. 21, no. 7, Mar. 2022. DOI: 10.5334/dsj-2022-007.
N. Krans, A. Ammar, P. Nymark, E. Willighagen, M. Bakker, and J. Quik, “Fair assessment tools: Evaluating use and performance,” NanoImpact, vol. 37, p. 100 402, 2033. DOI: 10.1016/j.impact.2022.100402.
C. Bahim, M. Dekkers, and B.Wyns, “Results of an Analysis of Existing FAIR Assessment Tools,” Zenodo, May 2019. DOI: 10.15497/rda00035.
E. Gonz´ález, A. Ben´ıtez, and D. Garijo, “FAIROs: Towards FAIR Assessment in Research Objects,” in Linking Theory and Practice of Digital Libraries, G. Silvello, O. Corcho, P. Manghi, et al., Eds., Cham: Springer International Publishing, 2022, pp. 68–80. DOI: 10.1007/978-3-031-16802-4_6.
D. Slamkov, V. Stojanov, B. Koteska, and A. Mishev, “A Comparison of Data FAIRness Evaluation Tools,” in Ninth Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications (SQAMIA 2022), Oct. 2022. [Online]. Available: https://www.researchgate.net/publication/364308377.
C. Sun, V. Emonet, and M. Dumontier, “A comprehensive comparison of automated FAIRness Evaluation Tools,” Semantic Web Applications and Tools for Healthcare and Life Sciences (SWAT4HCLS), Dec. 2022, Additional material at https://doi.org/10.5281/zenodo.5539823. [Online]. Available: https://ceur-ws.org / Vol-3127/paper-6.pdf.
E. Kontsioti. “The Road to FAIRness: An Evaluation of FAIR Data Assessment Tools.” (2023), [Online]. Available: https://www.thehyve.nl/articles/evaluation- fairdata-assessment-tools (visited on 04/26/2023).
European Commission and Directorate-General for Research and Innovation, J. M. Aronsen, O. Beyan, et al., Recommendations on FAIR metrics for EOSC, S. Jones and F. Genova, Eds. Publications Office, 2021. DOI: doi/10.2777/70791.
M. D. Wilkinson, S.-A. Sansone, E. Schultes, P. Doorn, L. O. Bonino da Silva Santos, and M. Dumontier, “A design framework and exemplar metrics for FAIRness,” Scientific Data, vol. 5, no. 1, Jun. 2018. DOI: 10.1038/sdata.2018.118.
A.-L. Lamprecht, L. Garcia, M. Kuzak, et al., “Towards FAIR principles for research software,” Data Science, vol. 3, no. 1, pp. 37–59, Jun. 2020. DOI: 10.3233/DS-190026.
N. P. Chue Hong, D. S. Katz, M. Barker, et al., “FAIR Principles for Research Software (FAIR4RS Principles),” Zenodo, 105 2022. DOI: 10.15497/RDA00068.
M. Barker, N. P. Chue Hong, D. S. Katz, et al., “Introducing the FAIR Principles for research software,” Scientific Data, vol. 9, no. 622, pp–pp, Oct. 2022. DOI: 10.1038 /s41597-022-01710-x.
D. S. Katz, M. Gruenpeter, and T. Honeyman, “Taking a fresh look at FAIR for research software,” Patterns, vol. 2, no. 1, Mar. 2021. DOI: 10.1016/j.patter.2021.100222.
FAIRplus. “FAIRplus Indicators V0.1.” (Oct. 2020), [Online]. Available: https://www .webaddress.com (visited on 04/26/2023).
M. D. Wilkinson, M. Dumontier, S.-A. Sansone, et al., “Evaluating FAIR maturity through a scalable, automated, community-governed framework,” Scientific Data, vol. 6, no. 174, Sep. 2019. DOI: https://doi.org/10.1038/s41597-019-0184-5.
FAIR Metrics Group. “Github fairmetrics.” (2022), [Online]. Available: https://github.com/FAIRMetrics/Metrics (visited on 04/26/2023).
RDA FAIR Data Maturity Model Working Group, “FAIR Data Maturity Model: specification and guidelines,” 2020. DOI: 10.15497/rda00050.
C. Bahim, C. Casorr´an-Amilburu, M. Dekkers, et al., “The FAIR Data Maturity Model: An Approach to Harmonise FAIR Assessments,” Data Science Journal, vol. 19, no. 1, pp. 1–7, Oct. 2020. DOI: 10.5334/dsj-2020-041.
A. Devaraju, R. Huber, M. Mokrane, et al., “Fairsfair data object assessment metrics (0.5),” Zenodo, Apr. 2022. DOI: 10.5281/zenodo.6461229
Downloads
Published
How to Cite
Conference Proceedings Volume
Section
License
Copyright (c) 2023 Mario Moser, Jonas Werheid, Tobias Hamann, Anas Abdelrazeq, Robert H. Schmitt
This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2023-06-29
Published 2023-09-07
Funding data
-
Deutsche Forschungsgemeinschaft
Grant numbers 442146713
