Sustainable Catalyst Data Infrastructure
Catalyst Data
Catalyst Data is the canonical data-contract, validation, persistence,
migration, import, source, provenance, and evidence-chain layer
for the Sustainable Catalyst platform.
It gives products a shared language for entities, indicators, observations,
measurements, reporting periods, datasets, methods, sources, imports,
transformations, evidence records, validation states, and reusable analytical workflows.
v1.3.0 — Sources, Provenance, and Evidence Chain.
The data layer preserves what a record represents, where it came from,
how it was transformed, which validation rules were applied,
and which downstream outputs depend on it.
Platform overview
A governed system of record for shared platform evidence
Catalyst Data is no longer only a conceptual SQL layer.
It provides a versioned contract, validation engine, persistent repository,
migration system, import pipeline, source registry, provenance chain,
and reusable evidence model for connected Sustainable Catalyst products.
Define
Use one canonical data contract
Standardize entities, indicators, periods, observations,
measurements, methods, sources, imports, relationships, and evidence state.
Validate
Reject malformed or incompatible records
Apply schema, type, identifier, range, relationship,
temporal, provenance, and contract-level validation before persistence.
Preserve
Store durable records and migrations
Maintain repository state, import history, migration paths,
version compatibility, archives, and repeatable data operations.
Trace
Follow evidence from source to output
Connect source records, transformations, methods,
observations, measurements, derived indicators, claims, and downstream use.
Live workspace
Create a traceable Catalyst Data record
Use the browser-based workspace to define an entity, indicator,
reporting period, measurement, source, method, confidence,
validation state, and review status in one structured record.
do not enter credentials, private source URLs, proprietary datasets,
client records, regulated information, or sensitive personal data.
A structured record improves traceability but does not verify truth automatically.
Catalyst Data Record 1.0
Build a Canonical Evidence Record
Create a versioned measurement record with stable identifiers, source provenance, method limitations,
confidence, and review metadata. The generated JSON follows catalyst-data-record/1.0.
Current capabilities
Seven layers of governed data infrastructure
Catalyst Data v1.3.0 combines repository integrity,
canonical contracts, validation, persistence, migrations,
imports, sources, provenance, and evidence-chain records.
01 · Contract
Canonical data contract
Define shared record shapes, required fields, identifiers,
enums, relationships, validation expectations, and version boundaries.
02 · Validation
Contract and rule engine
Validate schemas, types, identifiers, required relationships,
temporal logic, value ranges, provenance, and compatibility.
03 · Repository
Persistent data storage
Store canonical records, maintain indexes,
preserve current state, support queries, and retain durable identifiers.
04 · Migrations
Versioned schema evolution
Upgrade earlier records, preserve compatibility,
record migration history, and prevent silent structural drift.
05 · Imports
Controlled ingestion pipeline
Parse, normalize, validate, deduplicate, reject,
persist, and report imported records with bounded failure handling.
06 · Sources
Source registry and access context
Preserve source identity, provider, retrieval context,
license or access notes, timestamps, method relevance, and status.
07 · Evidence
Provenance and evidence chain
Connect source records, transformations, observations,
measurements, indicators, claims, reports, and downstream decisions.
08 · Interoperability
Portable records and product handoffs
Export governed JSON records and exchange packages
that preserve product, contract, version, status, method, and provenance.
Canonical data model
Separate identity, observation, measurement, method, and source
Catalyst Data keeps distinct record types for what something is,
what was observed, how it was measured or computed,
when it applies, and where the supporting evidence came from.
Identity
Entities
Organizations, geographies, projects, programs,
instruments, facilities, systems, people, topics, and other governed identities.
Time
Periods and temporal context
Reporting windows, observation times, baseline periods,
intervals, update cycles, effective dates, and temporal precision.
Definition
Indicators and metrics
Names, definitions, units, methods, formulas,
assumptions, interpretation limits, and version context.
Observed state
Observations
Directly recorded events, states, readings,
classifications, facts, or provider-supplied values with source context.
Computed state
Measurements
Numeric or categorical values tied to an entity,
indicator, period, method, source, confidence, and validation state.
Method
Transformations and calculations
Procedures, formulas, mappings, aggregation,
normalization, assumptions, software versions, and derivation records.
Origin
Sources
Datasets, documents, APIs, institutions, reports,
publications, instruments, files, retrieval events, and access notes.
Lineage
Provenance records
Who or what created the record, when it changed,
which transformation occurred, and which prior records it depends on.
Use
Evidence links
Connect records to claims, charts, reports,
experiments, decisions, publications, support records, and public outputs.
Import and validation workflow
From external record to governed repository state
Ingestion is treated as a visible, repeatable process rather than
a one-time file upload or silent transformation.
- 01Receive
Accept a file, API response, form record, or product exchange package.
- 02Identify
Determine source, contract version, record type, and import context.
- 03Parse
Read the payload and preserve the original import evidence where appropriate.
- 04Normalize
Map fields, identifiers, units, dates, enumerations, and relationships.
- 05Validate
Apply contract, type, range, temporal, relationship, and provenance checks.
- 06Deduplicate
Identify repeated, conflicting, superseded, or previously imported records.
- 07Persist
Write accepted canonical records and preserve rejection or warning details.
- 08Report
Return import counts, errors, warnings, provenance, and next actions.
Sources, provenance, and evidence
Preserve the path from source to claim
Catalyst Data connects the original source and import event
to each normalized record, transformation, observation,
measurement, derived indicator, report, claim, and decision that depends on it.
a downstream claim should not point only to a number.
It should be possible to inspect the number’s definition,
source, period, method, transformation history, and review state.
Platform connections
How Catalyst Data supports the current platform
Catalyst Data remains a distinct product while providing governed
records and evidence chains to research, intelligence, analytical,
scientific, decision, publication, support, and infrastructure systems.
Knowledge and sources
Knowledge Library
Exchange documents, source records, citations, quotations,
evidence links, relationships, collections, and publication context.
Research routing
Research Librarian
Route data needs, source discovery, missing evidence,
related records, research paths, and product-specific data questions.
Public observations
Site Intelligence
Normalize countries, indicators, events, observations,
source metadata, freshness, connector status, and public briefing data.
Analysis
Workbench
Supply validated entities, measurements, units,
methods, assumptions, datasets, and evidence links for calculation and modeling.
Scientific work
Research Lab
Preserve datasets, observations, instrument records,
experiment runs, validation outputs, methods, and scientific provenance.
Decision evidence
Decision Studio
Attach validated measurements, indicators, sources,
calculations, confidence, uncertainty, and evidence chains to Decision Packets.
Design records
Catalyst Canvas
Exchange stakeholder evidence, assumptions,
experiment records, criteria, observations, results, and design decisions.
Product learning
Product Support and Feedback
Connect support demand, known issues, failed searches,
feature suggestions, release context, and product-intelligence records.
Shared infrastructure
Platform Core
Register entities, exchange contracts, APIs, evidence records,
source controls, typed handoffs, trust metadata, and compatibility state.
Outputs and interoperability
Portable data records for connected use
Catalyst Data supports machine-readable exchange,
human-readable review, import diagnostics,
evidence inspection, and downstream product integration.
Canonical
Validated JSON records
Contract-conformant entities, indicators, periods,
observations, measurements, methods, sources, and provenance records.
Repository
Persistent queryable state
Durable records, indexes, relationships,
current-state pointers, migration history, and repository metadata.
Import
Diagnostics and rejection reports
Accepted, rejected, duplicate, warning,
migration, source, provenance, and validation results.
Exchange
Product handoff packages
Target-specific records preserving source product,
version, contract, artifact type, method, status, and evidence context.
Governance and reliability
Data quality is visible, versioned, and reviewable
Catalyst Data treats validity, provenance, confidence,
warnings, migration, access context, and evidence state
as first-class records rather than hidden implementation details.
Integrity
Repository and package contracts
Version checks, package contracts, release validation,
fixture conformance, syntax checks, and repository integrity.
Quality
Validation states and warnings
Valid, invalid, partial, warning,
rejected, superseded, migrated, and review-required states remain explicit.
History
Migrations and transformation records
Preserve structural upgrades, field mappings,
normalization, derivation, reprocessing, and compatibility history.
Access
Source and licensing context
Preserve access notes, provider context,
retrieval time, license or reuse constraints, and public/private boundaries.
Boundaries
Governed infrastructure, not automatic truth or unlimited data access
Catalyst Data provides contracts, validation,
persistence, ingestion, sources, provenance, and evidence chains.
It does not eliminate the need for source evaluation or qualified judgment.
Not a dashboard
The product owns records, not presentation alone
Dashboards, reports, maps, charts, and interfaces
can consume Catalyst Data but are not the data layer itself.
Not a data vendor
No proprietary feed is implied
The system can ingest and reference public,
licensed, private, institutional, or product-generated data,
but does not grant rights to external data.
Not automatic truth
Validation does not prove factual correctness
A record may satisfy the contract while still depending
on incomplete, outdated, biased, disputed, or methodologically weak evidence.
Not unlimited ingestion
Imports remain bounded and governed
File size, schema, access, licensing, privacy,
security, performance, and retention constraints still apply.
Not silent transformation
Derived values require method records
Normalization, aggregation, imputation, classification,
scoring, and inference should preserve method and provenance context.
Not professional substitution
Qualified review may be required
Legal, financial, medical, engineering,
safety-critical, regulated, and assurance uses require appropriate expertise.
Next step
Use Catalyst Data as the platform’s governed evidence backbone
Begin with a structured record, validate it,
preserve its source and provenance, connect it to downstream evidence,
and exchange approved records with the wider platform.