E2 · Publication Volume 24

Metadata and Data Dictionaries

field definitions, domains, CRS, units, owners and quality

Learning objectives

  • Explain why field definitions, domains, CRS, units, responsible roles and quality require explicit semantic modelling.
  • Design identities, relations and constraints that preserve metadata and data dictionaries across exchange.
  • Separate hard release gates from diagnostic metrics and interpretation choices.
  • Produce a bilingual-ready metadata record and machine-testable field dictionary from synthetic evidence.

The lesson is complete only when the learner can defend both the model and the release decision. A neat schema without evidence, tests or declared limitations is an unverified design. The assessed artefact must make assumptions visible and distinguish source assertions from derived conclusions.

Decision context

Document what a consumer must know without private conversation. Resource metadata answers what, where, when, why, how and under which constraints. Field definitions answer exactly what each value means and which values are valid. Both must be versioned alongside the data they describe.

Start with a decision record: name the intended use, the evidence required, the consequence of error, the accepted uncertainty and the role authorised to accept residual risk. Then ask whether the proposed model can answer the decision question without relying on filename conventions, row order, undocumented defaults or someone’s memory. This prevents technology selection from concealing a missing semantic requirement.

The same record may be fit for one use and unfit for another. A rapid exploratory view can tolerate conditions that a released exchange package cannot. Fitness is therefore stated against a use, contract version and quality gate rather than attached permanently to the data.

Core concept

Metadata makes a resource discoverable and interpretable; a data dictionary makes fields testable. Useful documentation connects resource-level purpose and extent to field-level definition, type, unit, domain, null policy, coordinate reference, quality rule and responsible role.

The working scope is field definitions, domains, CRS, units, responsible roles and quality. For each item in that scope, distinguish the thing itself, the label used by a source, the claim made about it and the record that carries the claim. Identity is not a display name; a value is not its unit; an observation is not a model; current is not the same as valid. These distinctions create explicit places for correction, uncertainty and competing interpretations.

A good semantic design can be explained as a set of sentences before it is encoded. Each sentence identifies a subject, a property or relationship, an object or result, and the context under which the claim holds. Physical tables and files are then projections of those sentences, not their source of meaning.

Semantic model

At resource level record identifier, title, description, purpose, spatial and temporal extent, update policy, distributions, licence or access statement, provenance and contact role. At field level record stable field identifier, label, definition, data type, cardinality, unit, domain, value-state policy, examples, validation and change history.

Test every proposed record against seven questions: What has identity? What type is it? Which property or relationship is asserted? Which spatial and temporal context applies? Which state or qualifier modifies the assertion? Which evidence supports it? Which version and activity produced the stored representation? Missing answers become explicit contract gaps.

Normalisation is used to separate independent facts, not to maximise the number of tables. A compact nested object can be semantically sound if the same identities, constraints and provenance remain explicit. Conversely, a highly normalised database can still be ambiguous when relationships and units exist only in documentation or application code.

Constraints and invariants

| Invariant | Executable or review test | | --- | --- | | Definitions are operational | A definition can be converted into examples and validation rules. | | Field identity survives renaming | Labels may change while a stable field identifier persists. | | Coordinate reference is complete | Declare horizontal and vertical reference, axes, units and epoch when relevant. | | Quality is scoped | State measure, result, evaluation method, date and applicable extent. |

An invariant is a condition that must remain true across storage, export, correction and reprocessing. Implement it as close to the authoritative boundary as practical and repeat the check at exchange boundaries. Record rule identifier, version, severity, evaluated scope, observed value and outcome so a failure can be reproduced.

Hard gates protect identity, semantic validity, required provenance and authorised use. Diagnostic checks reveal unusual values or patterns but require interpretation. Never convert a diagnostic threshold into deletion or correction without a reviewed rule and preserved source evidence.

Quantitative reasoning

Measure documentation coverage at required-element level: C_m = n_v / n_r, with separate hard gates for identifier, definition, type and unit where applicable. Add conformance rate for machine-testable constraints and freshness lag between data release and matching metadata release.

Every reported ratio states its numerator, denominator, exclusions and evaluation time. Stratify results by source, entity type, contract version or processing run where aggregation could hide a local failure. Counts accompany percentages so a seemingly large change based on a tiny denominator remains visible.

Precision is part of meaning. Do not add decimal places merely because a storage type permits them, and do not round identity, interval or coordinate fields without a declared tolerance and test. Quantitative summaries support a release decision; they do not replace semantic review.

Evidence and uncertainty

Metadata quality is evaluated by use, not field population alone. Test whether an independent consumer can locate the intended distribution, interpret representative fields, identify coordinate reference, apply value-state rules and trace provenance. Record unresolved ambiguity as a quality issue.

Build an evidence packet containing preserved source reference, acquisition or assertion context, applicable method, validation results, reviewer decision and links to derivatives. Classify uncertainty as observational, semantic, structural, parametric or policy-related where that distinction changes treatment. “Unknown” is a valid state when the evidence cannot justify a stronger claim.

Contradictory evidence remains available. The model may select one current assertion, but the reason, competing assertion and effective time are retained. This makes later reinterpretation possible without pretending the earlier evidence never existed.

Interfaces and storage

Publish human-readable documentation and machine-readable metadata from one governed source. Give every schema and dictionary version a stable identifier. Distribution metadata links to exact files or services and reports media type, format profile, checksum, byte size and access conditions.

Design an interface from the logical contract outward. Specify identifiers, types, cardinalities, units, value states, coordinate and time references, version negotiation, validation behaviour and structured errors before choosing a serialisation. The physical representation then declares its mapping to those logical elements.

Storage optimisation may partition, compress, index or cache data, but it must not change identity or silently remove context. A derived representation points to immutable inputs and a processing manifest. A cache carries freshness and contract-version information and is never treated as the only evidence copy.

Governance and access

Responsible roles review definitions, domains and quality rules. A field change proposal includes semantic effect, compatibility classification, migration, examples and affected consumers. Documentation release is part of the data release gate, never an optional later task.

Governance is expressed through named roles, review states and versioned decisions, not through references to a particular organisation. Define who may propose, validate, approve, supersede and withdraw each governed resource. The audit trail records the role and event while avoiding unnecessary personal data.

Apply least-necessary access to source evidence and derivatives. Access controls must not erase identifiers, lineage or quality metadata needed to understand an authorised release. When policy is unresolved, quarantine the output with a precise reason and escalation route.

Integration checkpoint

Resource metadata connects to field-level data contracts
Resource metadata connects to field-level data contracts

The diagram summarises the control flow for this lesson. Read it from source evidence through semantic structure and validation to a decision-ready artefact. Each arrow should correspond to a declared relationship or transformation; each boundary should have a contract; each released node should have an identity, version and provenance pointer.

Integrate the lesson by adding a bilingual-ready metadata record and machine-testable field dictionary to the evolving synthetic data package. Verify that earlier artefacts still resolve and that the new model does not overwrite observations, identifiers, values or versions introduced in previous lessons. Record every changed assumption.

Synthetic worked example

A synthetic collar table has columns X, Y and Z but no coordinate metadata. The learner refuses to label them east, north and elevation from convention alone. The source evidence establishes axes, units, horizontal reference and local vertical reference; the dictionary records them and adds range plus cross-field checks.

Work the example in four passes:

  1. Preserve the received records and write the intended decision without correcting anything.
  2. Identify entities, claims, context, uncertainties and policy constraints; mark every unresolved item.
  3. Apply the versioned rules, create derivatives and record the exact transformation plus validation evidence.
  4. Issue an accept, reject or quarantine decision and show how an independent reviewer can reproduce it.

Because the example is entirely synthetic, its values demonstrate method only. The important result is the chain from received evidence to justified decision. If a required fact is absent, the worked solution records the gap rather than manufacturing a plausible value.

Practice task

Author resource metadata and a dictionary for a synthetic spatial table. Include stable field identities, operational definitions, units, coordinate reference, domains, value states and quality rules. Ask a second reader to interpret five records using only the package and record every question they still need to ask.

Use the following acceptance criteria:

  • All required identifiers and references resolve to declared types.
  • Every transformation preserves the received evidence and records its derivation.
  • Invalid, unknown and inapplicable states remain distinct and machine-testable.
  • The output identifies the contract, vocabulary and processing versions used.
  • A second reader can reproduce the validation result without private knowledge.

Submit the source snapshot, authored contract or model, validation output, derivative, manifest and a short decision record. A screenshot alone is insufficient because it cannot demonstrate the exact input, version or rule execution.

Common failure modes

  • A column label is treated as a complete definition.
  • Coordinate reference is reduced to a short informal name.
  • Metadata describes a current dataset but is attached to an older file.
  • Quality is stated as good without measure, method or extent.

These failures share a pattern: convenient representation is mistaken for verified meaning. Diagnose the earliest boundary at which an assumption became implicit. Correct by restoring source evidence, making the assumption a versioned field or rule, rerunning dependent transformations and superseding—not overwriting—the affected release.

Do not repair a failure by adding an undocumented default. A blocked result with a specific missing dependency is safer and more reusable than a complete-looking result whose meaning cannot be reconstructed.

Review questions

  1. How do resource metadata and a data dictionary differ?
  2. What makes a field definition operational?
  3. Which elements make coordinate metadata complete?
  4. How can documentation quality be tested with consumers?

For each answer, identify the governing invariant, the evidence needed to evaluate it and the appropriate release behaviour when the invariant fails. A strong answer distinguishes scientific uncertainty from missing semantics and distinguishes a recoverable warning from a hard contract violation.

Sources and further reading