Application of 'RDA FAIR Data Maturity Model' to assess the PID registration service in terms of FAIRness

Introduction: Assigning a PID to a whole dataset, as common practice within research data management, is not enough to unambiguously identify the piece of information used and ensure the data citation properly and, consequently, promote the accreditation of research results. Particularly in the Social Sciences, PIDs are only available at the study level but not at the level of the inline data objects, such as survey variables. Since citing research data is the backbone of proper data reuse, our approach proposes an infrastructure to reference specific attributes within data sets, assigning PIDs to the fine-grained granularity level of attributes. By assigning PIDs to these attributes, individual elements of the data files can be referenced and retrieved with the required metadata for machine-actionable and human access. Methodology: Our approach to maturity level assessment relied on the RDA recommendation FAIR Data Maturity Model, an output of the FAIR Data Maturity Model WG. The solution was evaluated under the core criteria proposed by the cited framework to implement a level of the FAIR data principles. We assessed the service under the FAIR Data Maturity Model (RDA Working Group on FAIR Data Maturity Model, 2020, see DOI: 10.15497/rda00050), applying the stricter evaluation method on each indicator, assessing them by passing or failing binary answers. This approach was selected because the PID registration service is a widening solution to an established service through da|ra (da-ra.de). Results: The results demonstrate 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. Our service meets all indicators classified as essential. 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). Future work: We intend to address automatic features such as getting data automatically from a given dataset. Due to the high relevance of the service for implementing FAIR, we aim to provide reusable and generalised components as a blueprint for other projects.
The service is part of the KonsortSWD project deliverable (Persistent Identifiers for Variables - KonsortSWD Task Area 5: Measure 1), NFDI funding number 442494171. PID Service report https://doi.org/10.5281/zenodo.6397367.