AI-ready metadata with DDI and ISO/PAS 25955
An AI assistant asked to compare household income across two surveys needs to know what each survey measured. Was income recorded before or after tax? For a person or a household? As an amount or a category? A column name rarely answers those questions.
Colectica uses the Data Documentation Initiative (DDI) standards to connect data with its definitions, source questions, code lists, and processing history. Making that metadata available to AI tools gives them evidence to work with and gives researchers a way to check the answers. The useful test of “AI-ready” metadata is whether it can answer the questions that matter for a particular dataset and task.
What a column name leaves out
Consider this hypothetical variable from a household survey:
| Variable | HHINCBAND: annual gross household income band |
|---|---|
| Unit and population | Households living in private dwellings in the study area |
| Reference period | Calendar year 2025 |
| Source question | "Which band includes your household's total income before tax during 2025?" |
| Value representation | Ordered categories defined by a code list; for example, 3 means USD 50,000 to less than USD 100,000 |
| Missing values | -9 means the respondent refused to answer |
| Source and version | A reference to the question used in the 2026 survey instrument, including its identifier and version |
Here, 3 is a category code, not three dollars. Averaging the codes would not produce average household income, and treating -9 as a measured value would be an error. Comparing this variable with monthly net personal income would require further decisions, even if both columns were labeled “income.”
These are ordinary documentation questions. They become especially important when software selects variables or drafts analysis code on a researcher’s behalf.
How DDI records the context
DDI Lifecycle separates several ideas that are often compressed into a variable label. Its variable model distinguishes a Conceptual Variable, which links a concept and a universe (the population to which it applies); a Represented Variable, which adds a reusable value representation; and a Variable, which describes its use in a particular data context. This makes it possible to share definitions while recording differences between datasets. The DDI data-description guide explains these relationships and their connection to ISO/PAS 25955:2026 and ISO/IEC 11179.
The surrounding metadata matters just as much:
- Question wording and routing. A response depends on what was asked and who reached the question. DDI describes reusable questions separately from their placement and routing in an instrument. See the DDI data-capture guide.
- Codes, units, and missing values. A variable’s representation distinguishes categories, numeric values, dates, and other response types. Documented missing-value codes let software distinguish refusals and inapplicable responses from measured values.
- Sources and transformations. References can connect a variable to source questions, source variables, and processing instructions. These help explain how a derived variable was produced. The DDI Lifecycle 3.3 Variable specification describes these fields and references.
- Languages and shared terminology. Multilingual text and references to concepts and controlled vocabularies preserve more context than labels alone. DDI Lifecycle also supports reuse and versioning across repeated studies, as described in the DDI Alliance’s product overview.
For an AI-assisted comparison, these relationships provide a route from a search result to the evidence needed to assess it. Similar wording can identify candidates; deciding whether two measures are comparable still requires checking their definitions, populations, periods, and representations.
ISO/PAS 25955 and FAIR
ISO/PAS 25955:2026 describes the shared, interoperable features of the DDI family of standards and products. DDI Lifecycle provides the detailed model and structures used to document studies, questionnaires, and data across the research lifecycle. The Lifecycle specification is the reference for implementing those structures and following relationships between items.
There is also a useful connection to the FAIR principles: making data and metadata Findable, Accessible, Interoperable, and Reusable. The original FAIR paper calls for rich metadata, persistent identifiers, shared vocabularies, qualified references, and provenance, with explicit attention to machine use. Those practices are directly useful when preparing metadata for AI applications. FAIR access can include authentication and authorization; it does not require publishing confidential records.
Using metadata in an AI workflow
Retrieval-augmented generation
Retrieval-augmented generation (RAG) supplies a language model with relevant retrieved material when it produces an answer. The original RAG paper combines a language model with retrieval from an external information source.
For a statistical-data application, we recommend retrieving enough metadata to explain a search result: the variable definition, its universe and code list, relevant question text, and source references. An answer about income comparability should point to those records and identify unresolved differences. An integration should retain item identifiers and versions so a reviewer can return to the same evidence later.
This is a way to design an application around Colectica’s metadata. The choice of model, retrieval method, and evaluation process remains part of that application’s implementation.
Model Context Protocol
The Model Context Protocol (MCP) gives compatible AI applications a standard way to discover and call tools provided by an external server. An application can use those tools to retrieve information as part of a RAG workflow or a longer investigation.
In Colectica 8.0, Portal provides an MCP endpoint through which compatible AI tools can investigate repository concepts, questions, variables, and their relationships, with configurable OpenID Connect authentication. The 8.0 announcement describes this capability alongside Portal’s new discovery and lineage views.
For example, a researcher could ask an assistant to find questions about household income, examine their response categories, and summarize differences between studies. The useful result would include links to the supporting metadata and an explanation of any gaps. Metadata gives the assistant better evidence; the model’s interpretation still needs evaluation.
Maintaining the metadata in Colectica
The descriptions used by an AI application can be the same ones your survey authors, data managers, and researchers maintain in Colectica.
Designer supports importing and editing dataset documentation, linking concepts and questions, and applying metadata in bulk with Metadata Input Sheets. Questionnaires lets survey authors reuse repository questions, carrying shared metadata into instrument design.
Repository manages identified, versioned metadata items and their relationships. Its REST API and the Colectica SDK provide integration options for applications that need to search and retrieve those records. Preserve the agency, item identifier, and version when recording which metadata an analysis used.
Portal makes the documentation available for people to review. In version 8.0, its lineage views show source questions and variables, derived variables, and referenced versions. Researchers can follow these connections when checking an assistant’s explanation or investigating a dataset themselves.
Start with a question your researchers actually ask
Choose a small set of datasets and a concrete task, such as finding comparable income measures across survey waves. Review the definitions, populations, code lists, missing values, and source links needed to answer it. Then test whether an AI application retrieves the right records, cites the relevant versions, and recognizes when the documentation is insufficient.
That exercise gives you a practical way to prioritize metadata work and assess the integration. Contact us to discuss your existing documentation, or request an evaluation to explore the Colectica tools.
