The FAIR Data Maturity Model: RDA recommendations for FAIR assessment methods for all disciplines

This presentation is given during the RDA – SciELO 25 Years Seminar, Open Science Modus Operandi of Research Communication

Title: The FAIR Data Maturity Model: RDA recommendations for FAIR assessment methods for all disciplines (website)

Description: The importance of FAIR data is recognized worldwide. In this lecture we share the effort to ensure that FAIR dataset assessments developed for specific research communities are aligning to common recommendations. 

A critical element of research are the data used to determine the findings published in an article. These data along with the methods and software used, are valuable contributions to the scientific record. The broad research community has been working to improve the description, preservation and sharing of these digital objects so that they can be indexed, associated with the paper and creators, and potentially reused in future research. The FAIR Guiding Principles, published in 2016, initiated a moment of convergence across different disciplines motivating the development of assessment methods for researchers to use in order to ensure their data are FAIR – findable, accessible, interoperable, and reusable. The many data assessment methods were mostly unique to their community and not comparable across communities. As the number of assessment methods increased, members of the RDA felt it necessary to establish a “maturity model” that could be used to guide new assessment methods as well as compare current methods. 

In this lecture, co-chairs Shelley Stall and Edit Herczog, will give a brief history of the FAIR Data Maturity Model Working Group activities and provide an overview of the model elements.

Resources mentioned during the presentation:

RDA FAIR Data Maturity Model Working Group: https://www.rd-alliance.org/groups/fair-data-maturity-model-wg

FAIR Data Maturity Model Recommendation: FAIR Data Maturity Model Working Group. (2020). FAIR Data Maturity Model. Specification and Guidelines (1.0). https://doi.org/10.15497/rda00050

Publication highlighting the FAIR Data Maturity Model Recommendation: Bahim, C., Casorrán-Amilburu, C., Dekkers, M., Herczog, E., Loozen, N., Repanas, K., Russell, K. and Stall, S., 2020. The FAIR Data Maturity Model: An Approach to Harmonise FAIR Assessments. Data Science Journal, 19(1), p.41.DOI: https://doi.org/10.5334/dsj-2020-041

FAIR Guiding Principles: Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18

Detailed Description of Use Cases originally presented during an RDA Webinar:

• RDA FAIR Data Maturity Model- Aligning International Initiatives for Promoting & Assessing FAIR Data
• This webinar convened a group of experts representing national science organizations to discuss their current initiatives to enhance the FAIRness of the science within their communities applying different approaches and the use of the FAIR Data Maturity Model as a framework for comparing results.
• Video: https://youtu.be/rIMfwu7-hes

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