Session date and time: Breakout Session 1 – 12:00-13:30 BST, Tuesday, 6 October 2026

Collaborative session notes

Short Abstract

Data quality criteria are increasingly recognised as foundational components of research ecosystems and essential enablers of FAIR and Open Science practices, interoperability, and the production of reusable and trustworthy data. Approaches to assessing and communicating about data quality remain fragmented across disciplines, infrastructures, and initiatives, due to various sources of heterogeneity (e.g. definitions, methodologies, quality indicators, provenance practices, and governance frameworks)

The emergence of large-scale data aggregation approaches, notably driven by Artificial Intelligence and cross-domain analytics, as well as the integration of heterogeneous data for cross-domain research. This observation / evolution demands robust mechanisms to qualify, contextualise, and communicate the various levels and dimensions of data quality and data management quality across diverse sources and research ecosystems. 

This BoF aims to bring together communities involved in EOSC and equivalent international initiatives, CODATA, RDA, research and e-infrastructures, technology infrastructures, and domain-specific initiatives to discuss current efforts, identify common challenges, and explore opportunities for convergence around shared concepts, practices, and frameworks for Data Quality and Data Management Quality. The longer-term vision is to survey and characterise approaches to data quality assessment across research disciplines, identify commonalities, and formulate recommendations for communicating data quality through metadata with sufficient richness for both individual researchers and large-scale aggregators to assess the fitness-for-use of datasets.

The session will at least build on:

• the EOSC Data Quality TF activities and discussions around Data Quality and Data Management Quality, https://eosc.eu/advisory-groups/fair-metrics-and-data-quality 
• work conducted in the recently completed QUANTUM project (https://quantum.upv.es/) as a starting point for formalising quality dimensions and metrics, 
• the CODATA Task Group on Data Quality Management
  https://codata.org/initiatives/task-groups/research-data-quality-management-across-the-data-lifecycle/ ,
• the CDIF4EOSC project WP2 work on Data Quality,
• ISO/TC (technical committee) 211 for Geo-Informatics 
• previous community discussions on provenance, metadata quality, interoperability, uncertainty, and contextualisation.

The BoF will explore whether the community interest and maturity are sufficient to support the establishment of a future RDA Interest Group or Working Group on Data Quality and Quality of Data Management.

Motivation / Relevance to RDA

As research increasingly depends on interoperable, reusable data ecosystems, ensuring trustworthiness, comparability, contextualisation, and reproducibility becomes critical. While FAIR principles provide an important foundation, they do not fully address the operational and semantic dimensions of data quality and quality assessment processes, particularly in different contexts and across domains.

Various initiatives and communities are currently addressing different aspects of these challenges, often independently and with domain- or infrastructure-specific approaches:

• discipline-specific data quality frameworks,
• automated data quality evaluation,
• provenance approaches,
• uncertainty quantification,
• metadata quality assessment,
• Essential Variables initiatives,
• criteria related to data stewardship maturity and their integration into data quality assessment,
• minimum metadata requirements,
• quality indicators for data management processes,
• interoperability and semantic harmonisation efforts.

RDA provides an ideal cross-disciplinary environment to connect these initiatives and facilitate convergence toward common vocabularies, methodologies, and good practices.

The proposed BoF directly aligns with the P27 theme “Shaping a global open research commons” by addressing one of the core conditions for meaningful data sharing and reuse: trustworthy, contextualised, and interoperable data and data management practices. (openscience.lib.cas.cz)

Objectives

The session aims to:

1. Present several ongoing initiatives related to Data Quality and the Quality of Data Management.
2. Identify common challenges and gaps across disciplines and infrastructures.
3. Explore synergies between EOSC, CODATA, RDA, and other international and ’island our under represented specific’ initiatives (e.g. ISC World Data System, ISO or specific communities).
4. Discuss and create Concept Notes to the role of provenance, uncertainty, metadata quality, and contextualisation in quality (of data and data management) assessment.
5. Explore and map cross-domain approaches for harmonising quality concepts, variables, indicators, methodologies and management tools.
6. Evaluate community interest in establishing a future RDA group dedicated to these topics.

Expected Outcomes

• Identification of shared priorities and collaboration opportunities.
• Consolidation of an international community interested in Data Quality and Quality of Data Management.
• Initial mapping of existing initiatives, methodologies, and terminology.
• Roadmap toward an RDA Interest Group or Working Group.
• Identification of possible future collaborative outputs (landscape report, terminology harmonisation, recommendations, community paper, checklists etc.).
• Synergies with global initiatives on Data Quality (such as the CODATA Research Data Quality Management Across the Data Lifecycle Task Group) and local and domain-specific initiatives.
• Elaboration of funding mechanisms and lobbying for the demand of qualified training data for AI.

Target Audience 

These groups are not exclusive and can overlap

• Data stewards and curators
• Research Infrastructures and Technology Infrastructures
• Scientists (domain specific and interdisciplinary)
• EOSC and equivalent – CODATA and RDA communities
• Data quality practitioners
• Open science policy experts
• Metadata and interoperability specialists
• FAIR and STAP (FDO) practitioners
• Provenance and semantic web experts
• Statisticians and data analytics experts
• Decision Makers

Draft Agenda (90 min)

Introduction and session objectives (10 min)

• Context and rationale
• Positioning within EOSC / CODATA / RDA activities

Lightning presentations (30 min – TBC)

• CDIF4EOSC WP2 Data Quality activities
• CODATA TG on Data Quality Management Across the Data Lifecycle
• EOSC Data Quality discussions and TF outcomes
• QUANTUM Health Data Quality Project Outcomes (https://quantumproject.eu/) 
• ISO, Data Quality for Geo Information (ISO 19157-Part3)
• Previous initiatives on Data Management Quality
• Data quality requirements in practice and for AI-driven applications 

Community discussion (40 min)

Moderated discussion around:

• Definitions and terminology, identification of existing resources
• Provenance and documentation of quality assurance / control
• Uncertainty and uncertainty quantification
• Metadata quality
• Quality indicators and metrics
• Cross-domain interoperability
• Data management quality assessment
• Future coordination needs

Conclusions and next steps (10 min)

• Interest in future RDA group
• Identification of volunteers and contributors
• Next actions

Potential Organisers / Chairs 

• Romain David (ERINHA / EOSC TF on Data Quality – National Data Quality initiatives / RDA SG ig) – France / Belgium
• Simon Hodson (CODATA) – France
• Chris Schubert (CODATA – TG on Data Quality –  EOSC FAIR Assessment and Alignment- Area Expert Group – Chair EOSC TF on Data Quality) – Austria
• Kamil Dziubek (CODATA – TG on Data Quality) – Austria
• Thomas Exner (CDIF4EOSC) – Slovenia/Germany
• Alison Specht (University of Queensland) – Australia
• Marek Cebecauer (Heyrovsky Institute) – Czech Republic
• Alexandra Kokkinaki (BODC-NOC) – UK
• Steve McEachern (UK Data Service, UK / CODATA / DDI Alliance)
• Nick Juty (The University of Manchester) – UK

Keywords

Data Quality; Data Management Quality; FAIR; Provenance; Metadata Quality; Interoperability; EOSC; CODATA; Research Data Management; Uncertainty; Semantic Harmonisation, Provenance 

References

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