Evaluating compliance with FAIR Principles in research data infrastructure service via the RDA FAIR Data Maturity Model [Poster]
Creators: name: Saldanha Bach, Janete
name: Klas, Claus-Peter
name: Mutschke, Peter
Publisher: name: Zenodo
Year: 2023
Subjects: FAIR principles
FAIR assessment
RDA FAIR Data Maturity Model

description: Introduction


The FAIR Data Principles are widely applied to research data infrastructures, but assessing adherence to these principles remains challenging, especially for non-digital objects. Due to the interpretation scope of the FAIR principles, it is still difficult to evaluate the extent to which a data infrastructure service addresses the FAIR principles. The Research Data Alliance's FAIR Data Maturity Model (RDA-FDMM) (see DOI 10.15497/RDA00050) proposes 41 indicators to measure FAIRness. As the most prominent manual method, the RDA-FDMM stands out due to its completeness and comprehensive acknowledgement from the community and specialists. We share experiences assessing the KonsortSWD  PID service using RDA-FDMM. The RDA-FDMM defines indicators, priorities, and evaluation methods for FAIR principles, organized into three classes (Essential, Important, and Useful) and five levels.


Methodology: We applied RDA-FDMM to the KonsortSWD [MP1] (https://www.konsortswd.de/) PID service, aiming to assign PIDs to data elements below the study level (such as social science survey variables). The PID service, based on the data registration agency da|ra (www.da-ra.de), assessed some elements at the PID service level and others at the da|ra level. The assessment of the PID service complies highly with essential and important indicators (see DOI 10.5281/ZENODO.7409651). The RDA-FDMM measures indicators by answering binary questions, by "progress" evaluation or by a hybrid mode. We assessed the PID service by applying the stricter evaluation method on each indicator, assessing them by ”yes” or “no”  answers.


Results: The results show that the PID service meets all the indicators classified as essential and most of the indicators from the classes important and useful. It demonstrates outstanding achievements at levels 1 and 2, marking 100% on the assessment measure. The service achieves 88% compliance at level 3 and 89% at level 4. At level 5, the results show 80% of passed indicators (see DOI 10.5281/ZENODO.7409651). The indicator classes which do not meet the measures were four from the important and useful classified categories. However, it is essential to highlight the failed indicators concerned with automatic features, including references and/or qualified references to other data, and data is accessed automatically (i.e., by a computer program).


Conclusion: Our experience highlights the importance of evaluating also non-digital objects, apart from machine-readable objects. Not all components of the research data ecosystem are machine-readable since some FAIR principles’ aspects still require human mediation and interpretation. We recommend a "FAIR by design" approach from the beginning to ensure alignment with FAIR principles in project outcomes. Continuous assessments during the project’s lifetime promote ongoing research data infrastructure improvements on services.
description: KonsortSWD is funded by the German Research Foundation (DFG) within the framework of the NFDI – project number: 442494171.