CE-RISE Data Quality Framework Data Model
Cross-cutting utility data model for documenting data quality assessments, pedigree scores, diagnostics, remediation actions, provenance, and data-quality-derived uncertainty metadata for CE-RISE Digital Product Passport and Digital Material Passport data models.
Root container for data quality assessments and supporting records
Component, subsystem, product part, or process group
Derived indicator, score, or model output
Model, calculation workflow, or assessment
Portfolio, batch, or group-level aggregation
Hierarchical aggregation from inputs to profiles to overall indicators
Maximum risk or most severe issue determines the aggregate
Aggregation method has not been evaluated
Rule-based aggregation, such as green/orange/red decision rules
Overall score is determined by the worst criterion or limiting factor
Assessment is partially documented or partially verified
Assessment has been verified against supporting evidence
Reusable assessment describing the quality, fitness for purpose, diagnostics, and improvement needs of data used by CE-RISE models
Designed as the main integration point for other CE-RISE data models.
Other models may either embed this class or point to it through data_quality_assessment_identifier_value.
CE-RISE D2.4; W3C Data Quality Vocabulary; PROV-O; NUSAP; IPCC data quality guidance; ISO 14044; ISO 8000; ISO 9001
Assessment aggregating multiple lower-level assessments
Assessment of an input used to compute an indicator
Assessment of a derived indicator, score, or model output
Assessment of a data generation, collection, or processing process
Assessment of a record, object, or passport entry
Supporting evidence record for a data quality assessment, score, diagnostic, issue, remediation action, uncertainty translation, or aggregation
Calibration certificate or measurement-system evidence
Dataset snapshot, extract, or versioned source
Diagnostic report or quality check output
Harmonization, mapping, coding, or unit conversion log
Metadata record, data dictionary, or schema documentation
Remediation, mitigation, or improvement record
Standard, protocol, guide, or procedure document
Supplier declaration or third-party declaration
Validation, verification, or qualification report
Notebook, workflow, script, configuration, or reproducibility record
Administrative, operational, or management system record
Calculation, simulation, or model output
Database, inventory, registry, or factor set
Literature, report, publication, or secondary source
Sensor, IoT, telemetry, or monitoring source
Survey, interview, focus group, or qualitative source
Qualitative observations, narratives, claims, or interpretations
Semi-quantitative scores, categories, scales, or ratings
Comprehensive profiling of records, fields, distributions, and metadata
Distributional alignment, benchmark comparison, or drift detection
Impact quantification or sensitivity analysis
Missing-data characterization and mechanism analysis
Provenance correlation and causal inference
Foreign key, hierarchy, component sum, and relational consistency validation
Implementation reproducibility or workflow rerun check
Semantic, unit, coding, or label consistency verification
Schema, key, type, and structural integrity validation
Temporal or spatial alignment verification
Data are suitable for exploratory or preliminary use only
Data are fit only under stated conditions, limitations, or treatments
There is insufficient information to judge fitness
Fitness for purpose has not been evaluated
Issue remains but has been accepted with documented risk
Issue has been mitigated but not fully resolved
Aggregation of assessments, profiles, criterion scores, metrics, or uncertainty translations across values, records, datasets, or indicators
CE-RISE D2.4; W3C Data Quality Vocabulary
Closeness to true values, intended meanings, or accepted references
Extent to which required data, metadata, coverage, or contextual factors are present
Internal coherence and stability across sources, contexts, units, definitions, coders, or time
Repeatability, variability, or dispersion of repeated observations
Quality and auditability of origin, custody, and processing history
Degree to which data represent the target population, technology, geography, or time period
Degree to which the result can be reproduced from documented data and workflows
Temporal relevance, currency, update cadence, and version suitability
Degree to which methods, assumptions, provenance, and transformations are visible
Appropriateness of the data for the intended purpose or construct
Proceed; criteria are acceptable for the intended purpose
No decision flag because quality has not been evaluated
Review; one or more criteria require scrutiny, treatment, or caveats
Reject or pause; severe issue makes the data unsuitable without remediation
System boundary, scope, scenario, or model mismatch
Missing, expired, or inadequate calibration or traceability evidence
Coding, category, definition, or harmonization mismatch
Duplicate, contradiction, unresolved conflict, or inconsistent record
Invalid value, failed range check, or failed rule
Method mismatch or insufficient procedural fidelity
Missing values, missing metadata, or incomplete coverage
Sampling, source, geographic, temporal, technological, or population representativeness issue
Outlier, anomaly, discontinuity, or drift finding
Missing, incomplete, or unverifiable provenance
Schema drift, unexpected structure, or incompatible data contract
Outdated, stale, provisional, or insufficiently versioned data
Unit mismatch, conversion issue, or scale inconsistency
Score 1: very reliable; data meet the highest standards with minimal limitations
Score 2: reliable; data have minor limitations but remain fit for most purposes
Score 3: unreliable; substantial limitations require caution and remediation
Score 4: dubious; severe flaws make data unsuitable for formal decisions without remediation
Enforce schema, unit, encoding, or semantic data contracts
Create or update governance, access, data sharing, or reporting agreement
Apply missing-data treatment or imputation
Complete missing metadata, provenance, or evidence
Quarantine affected records, fields, or components
Recalibrate instrument, method, model, rating, or scale
Apply reweighting, propensity adjustment, or post-stratification
Redesign sampling, sourcing, or coverage strategy
Refresh outdated source data or update versions
Increase uncertainty or apply uncertainty factors to contain risk
Canonicalize units, labels, codes, or categories
Lookup from quality score to coefficient of variation range
Directly assigned coefficient of variation
Uncertainty factor interpreted on a lognormal scale
Monte Carlo simulation using data-quality-derived inputs
Sensitivity analysis used to translate data quality into uncertainty
Lookup from quality score to uncertainty factor
Data characterization records available for reuse by one or more assessments
Reusable data quality assessments that can be embedded or referenced by other CE-RISE data models
Evidence records referenced by the data quality framework
Data quality profiles available for reuse by one or more assessments
Aggregations of data quality assessments, profiles, criteria, metrics, or uncertainty translations
Translations from data quality scores to uncertainty information
Criterion-level assessment for a data quality dimension such as validity, accuracy, consistency, timeliness, or completeness
CE-RISE D2.4; NUSAP; W3C Data Quality Vocabulary
Diagnostic run or analysis that compares data against expectations, detects deviations, infers causes, and estimates impact
CE-RISE D2.4; W3C Data Quality Vocabulary; PROV-O
Identifier for an aggregation of data quality assessments, profiles, criteria, metrics, or uncertainty translations
References to the input assessments, profiles, criteria, metrics, or data sources used in the aggregation
Reference to the output assessment, profile, score, uncertainty translation, or decision produced by the aggregation
Rationale for the aggregation method and weighting choices
Weights or weighting rule used in the aggregation
Additional notes about the data quality assessment
Purpose for which the data quality assessment is performed
Person, organization, system, or workflow responsible for the data quality assessment
Lower bound of coefficient of variation derived from data quality information
Upper bound of coefficient of variation derived from data quality information
End date for the data collection or generation period
Method, protocol, procedure, system, or workflow used to collect or generate the data
Documented collection protocol, questionnaire, instrument, test method, API, or extraction logic
Start date for the data collection or generation period
Combined coefficient of variation after aggregating data-quality-derived uncertainty contributions
Combined uncertainty factor after aggregating data-quality-derived uncertainty contributions
Confidence level implied by the data quality assessment
Identifier for a criterion-level quality assessment
Impact of this criterion on the intended use, uncertainty, bias, comparability, or decision
Metric value, observation, or diagnostic result used to support the criterion score
Numeric criterion score when the score is represented as a number
Rationale for assigning the criterion score
Pedigree score assigned to a quality criterion
Threshold, rule, target, or expectation used to score the criterion
Weight assigned to the criterion in an aggregation or decision rule
Characterization of the assessed data and its provenance
Identifier for a data characterization record
Domain or subject area of the data, such as product specification, LCA, diagnostics, compliance, or circularity
Provider, owner, custodian, supplier, laboratory, or organization responsible for the data
Class in an external model to which this data quality assessment applies
Name, URI, or namespace of the external data model to which this data quality assessment applies
Identifier of the external record, object, dataset, or passport entry to which this data quality assessment applies
Slot or attribute in an external model to which this data quality assessment applies
Identifier of a specific value object when the target model gives value-level identifiers
JSONPath, XPath, RDF path, or other pointer to the value to which this data quality assessment applies
Date and time when the data quality assessment was issued or last updated
Identifier for a data quality assessment that can be referenced from another CE-RISE model
Human-readable label for the data quality assessment
Lifecycle status of the data quality assessment
Type of data quality assessment being documented
Date associated with supporting evidence
Provider, author, laboratory, supplier, or organization responsible for supporting evidence
Version, edition, or revision of supporting evidence
Data quality profile associated with an assessment
Identifier for a data quality profile aggregating one or more criterion assessments
Human-readable name of a data quality profile
Standard, guide, protocol, or scoring framework applied in the data quality assessment
Uncertainty factor derived from data quality information
Unit associated with a quantitative value or metric
Identifier for the assessed data source, dataset, value source, or information source
Name of the assessed data source, dataset, value source, or information source
Version, edition, revision, release date, or snapshot identifier for the assessed data
Plausible or confirmed cause of the observed diagnostic finding
Identifier for a diagnostic run, check, or analysis
Impact of the diagnostic finding on the intended analysis, decision, uncertainty, or comparability
Diagnostic method, test, script, notebook, query, or procedure used
Information needed to reproduce the diagnostic run, such as pinned dependencies, configuration, hash, or workflow version
Date and time when the diagnostic run was executed
Software, notebook, workflow, configuration, or service used to execute the diagnostic
Expected value, threshold, rule, contract, baseline, or target used by the diagnostic
Expected score, score change, or quality improvement after remediation
Overall statement of whether the data are fit for the stated purpose
Geographic coverage or spatial representativeness of the data
Harmonization, mapping, coding, unit conversion, or schema alignment applied to combine or compare data
Intended use, decision context, or analytical purpose for which data fitness is assessed
Risk statement explaining how the issue may affect the intended use, decision, or analysis
Root cause, suspected cause, or causal inference for the issue
Scope of the issue, such as affected fields, records, time periods, regions, suppliers, or processes
Severity or criticality of the data quality issue
Lifecycle status of the data quality issue
Criterion or criteria that limit the overall score, confidence, or decision flag
Method, query, diagnostic procedure, rule, or script used to compute the metric
Numeric value of a metric, indicator, diagnostic output, or score
Whether the metric passes the defined threshold or expectation
Textual, categorical, or structured value of a metric, indicator, diagnostic output, or score
Threshold, tolerance, expectation, or acceptance criterion for the metric
Observed deviation from the expectation, threshold, baseline, or target
Numeric quality score when the score is represented as a number
Overall pedigree score for the assessed data, typically from 1 very reliable to 4 dubious
Target population, inference scope, product scope, material scope, or process scope represented by the data
Preprocessing, cleaning, transformation, imputation, filtering, or normalization applied before assessment
Summary of the data quality profile, including main strengths and weaknesses
Narrative provenance of the data, including who collected it, when, under which protocol, and how it was processed
Operational decision flag derived from the quality profile
Identifier for a data quality issue, limitation, anomaly, or risk
Category or criterion to which the quality metric belongs
Identifier for a data quality metric or diagnostic metric
Human-readable name of the data quality metric
Identifier for a remediation action, mitigation, containment, or improvement activity
Completion date for the remediation activity
Description of the remediation, mitigation, containment, or improvement activity
Person, organization, team, system, or workflow responsible for remediation
Priority assigned to the remediation activity
Lifecycle status of the remediation activity
Sample size, number of observations, number of records, or number of contributing sources
Quality score used as input to the uncertainty translation
Technological, product, process, or system coverage represented by the data
Temporal coverage or period represented by the data
Distributional assumption used for data-quality-derived uncertainty, such as normal or lognormal
Identifier for a translation from data quality scores to uncertainty information
Method used to translate data quality information into uncertainty
Rationale explaining how data quality information was translated into uncertainty
Update cadence or refresh frequency for the data
Definition of the variable, field, indicator, category, or qualitative construct being assessed
Method or evidence used to verify that remediation was effective
Characterization of the assessed data, including source, provenance, scope, collection method, versioning, and preprocessing
Profile aggregating data quality criteria, scores, flags, and confidence for a dataset, value, source, or information product
Metric, indicator, diagnostic output, threshold check, or quality measurement supporting a criterion score
Remediation, mitigation, containment, or improvement activity for a data quality issue
Data-quality diagnostic runs, checks, or analyses
Data quality issue, limitation, anomaly, or risk identified through scoring, diagnostics, provenance review, or expert assessment
Operational decision flags derived from quality scores
Translation from data quality information into uncertainty metadata, such as coefficient of variation or uncertainty factor
Method used to aggregate criterion scores or lower-level assessments into a profile
Reference to a metrological traceability statement, chain, method, calibration, or boundary definition related to this data quality assessment
Quality metrics or diagnostic metrics used by profiles, criteria, or diagnostics
Remediation, mitigation, containment, or improvement actions
Confidence levels derived from data quality assessment
Data quality issues, limitations, anomalies, or risks
Severity values for diagnostics and quality issues
Reference to an uncertainty statement, uncertainty budget, or uncertainty component informed by this data quality assessment
DQA-to-uncertainty translation associated with an assessment or profile
Lifecycle status values for data quality assessments
Kinds of data quality assessments supported by this model
Types of data covered by data quality assessments
Methods for translating data quality into uncertainty
Levels at which data quality may be aggregated
Methods for aggregating data quality information
Data quality criteria supported by the framework
Types of remediation, mitigation, or improvement actions
Types of evidence used to support data quality assessments
Reference to supporting evidence such as a report, dataset, diagnostic output, certificate, standard, or workflow record