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Lightning talk

Enhancing FAIR Compliance in Research Data Infrastructures: Insights from Applications of the RDA FAIR Data Maturity Model and the F-UJI Automated FAIR Data Assessment Tool

26 September 2023| 16:00| Session 3| Sala Nouvel - Reina Sofia Museum

The FAIR Data Principles are widely applied to research data infrastructures, but assessing adherence to these principles remains challenging, especially for non-digital objects. The Research Data Alliance's FAIR Data Maturity Model (RDA-FDMM) proposes 41 indicators to measure FAIRness. We share experiences assessing KonsortSWD's PID service using RDA-FDMM and discuss automatic assessment using the F-UJI Tool, which employs RDA-FDMM and FAIRsFAIR Metrics in a machine-readable fashion.

The RDA-FDMM defines indicators, priorities, and evaluation methods for FAIR principles, organized into three classes (Essential, Important, and Useful) and five levels. We applied RDA-FDMM to KonsortSWD's PID service, aiming to assign PIDs to data elements below study level (such as survey variables). The PID service, an extension of the data registration agency da|ra , assessed some elements at the PID service level and others at the da|ra level. KonsortSWD's PID service assessment achieved high compliance with essential and important indicators.

The F-UJI Tool aims to provide automated FAIR assessment for research data from trustworthy repositories. It considers only indicators that can be assessed automatically, covering 16 of the 41 RDA-FDMM indicators. We used the F-UJI Tool to assess a GESIS dataset example and identified measures to improve FAIRness, increasing our research data score from 47% to 74%.

Automatic tools partially support FAIRness evaluation, as some aspects require human mediation and interpretation. However, tools like F-UJI are valuable for identifying weaknesses in metadata and metadata presentation that can be improved by automatic means.

The RDA-FDMM is a comprehensive standard for manual FAIR assessment recognized by the community and experts. Our experience highlights the importance of evaluating both machine-readable as well as non-machine-readable elements. Automated tools have limitations and technical challenges but offer valuable feedback for improvements. As the research ecosystem evolves, providing easily machine-readable metadata becomes increasingly important. We recommend adopting a "FAIR by design" approach early in product or service development to ensure FAIR principles are embedded in project outcomes and conducting regular FAIR assessments throughout the project lifetime to continuously evaluate and innovate research data infrastructures.

Presenter

Janete Saldanha Bach

Dr. Janete Saldanha Bach, GESIS – Leibniz Institute for the Social Sciences. Postdoc in the NFDI consortium KonsortSWD in the department "Knowledge Technologies for the Social Sciences", team FAIR Data, working in the consortia KonsortSWD Project of the National Research Data Infrastructure (NFDI). She holds a Ph.D. and a Master's degree in Science and Technology Studies (STS) and a bachelor's degree in Information Science. Her research expertise is in Open Science, especially in research data management and data reuse in the Social Sciences. She is currently involved in consortium KonsortSWD, Task Area 5 Measure 1 - developing the conceptual framework for the PID registration service at a variable level, Task Area 5 Measure 2 Enhancing data findability, and applying FAIR principles assessment models and tools. ORCID: https://orcid.org/0000-0001-9011-5837.

Claus-Peter Klas

Dr Claus-Peter Klas, GESIS – Leibniz Institute for the Social Sciences, Team Leader "Data & Service Engineering" and Measure Lead in the NFDI consortium KonsortSWD in the department "Knowledge Technologies for the Social Sciences". He received his PhD in computer science at the University of Duisburg-Essen and was a postdoctoral researcher in the Department of Multimedia and Internet Applications, Faculty of Mathematics and Computer Science, University of Hagen, Germany. His research focuses on information retrieval, interactive information retrieval, information systems, databases, digital libraries, preservation and grid and cloud architectures. He developed the software Daffodil founded on a national research project and worked on national and European research projects such as The European Film Gateway, SHAMAN (Sustaining Heritage Access through Multivalent ArchiviNg) and Smart Vortex (Scalable Semantic Product Data Stream Management for Collaboration and Decision Making in Engineering).  He is currently responsible for several infrastructure projects within GESIS, such as da|ra, SowiDataNet or Missy, all concerned with providing information and data for social scientists. In addition, he leads the measure PID Services in the national research infrastructure project NFDI. His team is developing an open-source DDI suite to support getting DDI into operation. ORCID: https://orcid.org/0000-0002-7794-7716.

Brigitte Mathiak

Brigitte Mathiak is a senior scientist at GESIS - Leibniz-institute for the Social Sciences with a PhD in Computer Science. Her research activities focus on data discovery, information extraction and research data management. She is the lead of the KonsortSWD measure on data findability. Deputy speaker of the NFDI section on (Meta)data, Terminologies and Provenance. Co-Speaker of the working group on Search & Harvesting in that section. Co-Chair of the GOFAIR Discovery Implementation Network and the RDA working group on data granularity. ORCID: https://orcid.org/0000-0003-1793-9615.

Yudong Zhang

Mr. Yudong Zhang is a Software Engineer in GESIS – Leibniz Institute for the Social Sciences, in the NFDI consortium KonsortSWD in the department "Knowledge Technologies for the Social Sciences", team FAIR Data, working in the consortia KonsortSWD Project of the National Research Data Infrastructure (NFDI). He holds a Master’s degree in Media Informatics, a bachelor's degree in Computer Science and a bachelor's degree in Accounting Banking and Finance. He is an expert in research data identify, management and exchange. He is currently involved in consortium KonsortSWD, Task Area 5 Measure 1 - developing the conceptual framework for the PID registration service at a variable level, Task Area 5 Measure 2 - Enhancing data findability, and applying FAIR principles assessment models and tools and Task Area 5 Measure 5 - Interface for Data Exchange. ORCID: https://orcid.org/0009-0006-7108-475X.

Peter Mutschke

Peter Mutschke is deputy head of the department “Knowledge Technologies for the Social Sciences (KTS)" and leader of the team "FAIR Data and Human Information Interaction" of KTS. His research interests include Information Retrieval, Network Analysis and Open Science. He worked in a number of national and international research projects, such as the DFG projects DAFFODIL and IRM and the EU projects WeGov, SENSE4US, OpenMinTeD and MOVING. Peter served as a member of the management committee of the Leibniz research alliance “Science 2.0/Open Science” from 2013-2021. He founded and coordinates the GO FAIR Implementation Network "Cross-Domain Interoperability of Heterogeneous Research Data (Go Inter)", and he is member of the steering committee of the FAIR Digital Objects Forum (fairdo.org) where he also co-chairs a working group on semantics. He is currently involved in consortia KonsortSWD, NFDI4DataScience and BERD@NFDI of the National Research Data Infrastructure (NFDI). ORCID: https://orcid.org/0000-0003-3517-8071.