Overview
ISO/PAS 25955:2026 - Information and documentation: Technical interoperability - Data Documentation Initiative (DDI) addresses the requirements and shared features that facilitate technical interoperability within the Data Documentation Initiative (DDI) family of standards and related products. The DDI standards are widely used to describe, document, and manage data-primarily across social, behavioural, and economic (SBE) research-and to support the entire data lifecycle. This specification outlines concepts, structures, and methodologies that enable consistent metadata frameworks, semantic interoperability, and model independence, making data more discoverable, comparable, and reusable across organizations and domains.
Key Topics
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Metadata Structure: DDI standards define frameworks to organize and manage metadata for data and related artifacts. These include both human-readable and machine-actionable metadata.
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Variable and Unit Cascades:
- Variable Cascade (VC) allows reusable, multilayered descriptions of variables to promote consistency across studies and datasets.
- Unit Cascade (UC) provides structured ways to describe populations and samples, facilitating comparability and integration.
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Value Domains: Specification of permissible values for variables through enumerated lists, numeric ranges, or formation rules. This supports precise data representation and validation.
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Data Lifecycle Coverage: DDI standards address the full data lifecycle: conceptualization, design, acquisition, processing, analysis, dissemination, and archiving.
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Model Independence: Adoption of Platform Independent Models (PIM) and Platform Specific Models (PSM) ensures that DDI standards are not constrained by specific technologies, enabling broad integration and flexibility (e.g., XML, JSON, RDF).
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Semantic Interoperability: The use of controlled vocabularies standardizes descriptions to ensure consistent meaning and interpretation of data terms.
Applications
ISO/PAS 25955:2026 and the DDI standards have practical value in a range of information management and data interoperability scenarios:
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Research Data Management: Used by universities, statistical agencies, and research organizations to document data structures, variables, and methodologies, ensuring high levels of data transparency and reuse.
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Data Integration and Sharing: Facilitates the merging and comparison of datasets from disparate sources by standardizing metadata and value domains, supporting data harmonization initiatives.
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Statistical Surveys: Empowers national statistical offices and institutions to manage demographic, economic, or social surveys efficiently and consistently by leveraging reusable metadata structures and controlled vocabularies.
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Long-term Archiving: Ensures data and metadata are preserved in a structured way, covering the needs of future researchers and supporting compliance with open data policies and archival standards.
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Cross-domain Data Interoperability: With the introduction of DDI-CDI, DDI standards now support integration with data outside the SBE domains, expanding practical usage to government, business, health, and environmental data management.
Related Standards
Several international standards and auxiliary DDI products enhance the implementation and utility of ISO/PAS 25955:2026:
- ISO 1087:2019 - Terminology work and terminology science – Vocabulary
- ISO/IEC Guide 2:2004 - Standardization and related activities – General vocabulary
- ISO 8601 - Date and time format
- ISO/IEC 11179-3:2023 - Metadata registries (MDR) – Metamodel for registry common facilities
- W3C XML, JSON, RDF - Key data serialization standards supporting model interchange
- DDI Suite:
- DDI-Codebook (DDI-C)
- DDI-Lifecycle (DDI-L)
- DDI-Cross Domain Integration (DDI-CDI)
- XKOS – eXtended Knowledge Organization System
- SDTL – Structured Data Transformation Language
- Controlled Vocabularies
Leveraging ISO/PAS 25955:2026 in conjunction with these related standards helps organizations meet interoperability, governance, and data stewardship goals, fostering richer collaboration, higher data quality, and streamlined workflows.