Data Harmonisation and Interoperability

1. Outcome

This Circular provides guidance on achieving data harmonisation and interoperability for ocean accounts, addressing the technical and methodological challenges of integrating data from diverse sources, formats, and domains. Data harmonisation underpins three critical decision contexts for ocean accounting: cross-agency data sharing within national statistical systems, international comparability of ocean accounts across jurisdictions, and dissemination of ocean accounting data to external users through standardised exchange platforms.

Cross-agency data sharing: Ocean accounts draw upon data from national statistical offices (economic statistics), environmental agencies (ecosystem monitoring), hydrographic organisations (bathymetry), fisheries management authorities (catch and effort data), and maritime administrations (vessel tracking). Without harmonised classifications, coordinate reference systems, and exchange formats, each bilateral data transfer requires bespoke transformation procedures. The standards described in this Circular enable agencies to establish common data integration protocols, reducing transaction costs and improving data quality through systematic quality checks.

International comparability: Countries implementing ocean accounts require comparable indicators for benchmarking, knowledge exchange, and coordination of regional ocean governance. The SDMX framework provides the international standard for exchanging statistical data, enabling ocean accounting indicators to be disseminated through the same infrastructure as national accounts, environmental-economic accounts, and SDG indicators. Alignment with geospatial standards from the Open Geospatial Consortium and International Hydrographic Organization ensures that spatial data components of ocean accounts can similarly be exchanged across jurisdictions.

Standardised dissemination: Publication of ocean accounting data through SDMX-compliant data repositories enables automated discovery and retrieval by policy analysts, researchers, and decision-support systems. Classification concordances ensure that data collected under national frameworks can be mapped to international reference classifications, maximising reusability while preserving national detail.

Readers will gain an understanding of the key international standards for statistical data exchange, including the Statistical Data and Metadata Exchange (SDMX) framework; geospatial interoperability standards from the Open Geospatial Consortium (OGC) and International Hydrographic Organization (IHO); and classification concordance mechanisms that enable mapping between different classification systems. The Circular explains how these standards support the compilation of coherent ocean accounts by enabling data from national statistical offices, environmental agencies, hydrographic organisations, and research institutions to be combined in a consistent manner. By applying the principles and protocols described here, compilers will be equipped to establish data integration workflows that maximise data quality while minimising manual processing.

This Circular builds upon the quality assurance principles established in TG-0.7 Quality Assurance and supports implementation of all thematic ocean accounting modules. All ocean accounts benefit from harmonised data, particularly TG-3.1 Asset Accounts which integrate physical and monetary data from multiple sources, TG-2.6 Physical Flow Accounts which depend on consistent classifications, and TG-3.8 Combined Presentations which integrate data across environmental, economic, and social domains. Key terms are defined in TG-0.6 Glossary.

2. Requirements

This Circular requires familiarity with:

• TG-0.1 General Introduction to Ocean Accounts -- for the conceptual framework and key components of Ocean Accounts, including the relationship between environmental and economic accounting frameworks that data harmonisation must bridge.

• TG-0.7 Quality Assurance -- for the quality dimensions and assessment frameworks applicable to ocean accounting data, particularly the coherence and comparability dimensions that data harmonisation directly supports.

3. Guidance Material

Ocean accounts draw upon data from an exceptionally diverse range of sources: fisheries catch statistics from national statistical offices; bathymetric surveys from hydrographic agencies; satellite observations from earth observation programmes; ecosystem condition assessments from environmental monitoring networks; and economic statistics from national accounts. Each of these data streams has evolved within its own institutional context, employing different standards, classifications, formats, and temporal and spatial reference systems. The fundamental challenge for ocean accounting is to harmonise these disparate data streams into a coherent accounting framework while preserving data quality and provenance. For guidance on spatial delineation of accounting units, see TG-1.3 Spatial Units; for temporal considerations, see TG-1.4 Temporal Considerations.

This section examines four interrelated dimensions of data harmonisation and interoperability: data standards that define common structures and semantics; exchange formats that enable machine-readable data transmission; interoperability protocols that support automated data discovery and retrieval; and classification concordances that enable translation between different coding systems. Together, these elements form the technical infrastructure for integrated ocean accounting.

Figure 4.6.1: Standards integration architecture for ocean accounts^[1]

3.1 Data Standards

Data standards provide the foundational agreements on how data should be structured, described, and interpreted. For ocean accounting, three families of standards are particularly important: statistical data standards, geospatial data standards, and marine domain-specific standards. The application of these standards supports the quality dimensions described in TG-0.7 Quality Assurance, particularly coherence and comparability.

Statistical Data and Metadata Exchange (SDMX)

The Statistical Data and Metadata Exchange (SDMX) initiative provides the international standard for exchanging statistical data and metadata^[2]. Developed by the seven sponsor organisations (BIS, ECB, Eurostat, IMF, OECD, United Nations, and World Bank), SDMX has been designated as an ISO International Standard (ISO 17369)^[3]. The SDMX 3.1 version, released in May 2025, introduces enhanced support for geospatial data, microdata, and improved data structure definitions^[4].

SDMX is built around a comprehensive information model that defines the structural metadata needed to describe statistical data^[5]. At its core is the Data Structure Definition (DSD), which specifies the dimensions, attributes, and measures that characterise a particular data collection. For ocean accounting, DSDs would define the structure of fisheries statistics, marine economic activity data, and environmental flow accounts. The structure of physical flow accounts is described in TG-2.6 Physical Flow Accounts.

The SDMX framework supports three process patterns for data exchange^[6]:

1. Bilateral exchange -- counterparties agree on specific formats and processes
2. Gateway exchange -- multiple organisations agree on a common exchange format
3. Data-sharing exchange -- open standards enable any organisation to access and use data

For ocean accounting, the data-sharing model is particularly relevant as it enables national statistical offices, environmental agencies, and research institutions to publish data in standard formats that can be automatically discovered and integrated.

SDMX provides three transmission formats: SDMX-ML (XML-based), SDMX-JSON (JSON-based), and SDMX-CSV (comma-separated values)^[7]. The CSV format is particularly useful for ocean accounting practitioners as it "can be readily created and interpreted by standard software tools such as Microsoft Excel" while still enabling lossless conversion to other formats^[8].

The SDMX registry services provide "visibility into the data and metadata existing within the community, and support the access and use of this data and metadata by providing a set of triggers for automated processing"^[9]. A registry-based architecture enables ocean accounting compilers to discover available data sources, retrieve structural metadata, and automate data collection workflows.

Geospatial Standards

The Global Statistical Geospatial Framework (GSGF), version 2.0 released by UN-GGIM and the Statistical Commission in 2025, provides the overarching framework for integrating statistical and geospatial information^[10]. The GSGF defines five principles, of which Principle 4 on "Statistical and geospatial interoperability" is directly relevant to ocean accounting data integration^[11].

Data interoperability, as defined in the GSGF, refers to "the ability of different systems, organizations, and applications to exchange, interpret, and use data in a coordinated manner"^[12]. The framework identifies four interoperability dimensions derived from the European Interoperability Framework (EIF)^[13]:

• Legal interoperability -- enabling organisations under different legal frameworks to share data
• Organisational interoperability -- aligning business processes and responsibilities
• Semantic interoperability -- exchanging data through common terminology and formats
• Technical interoperability -- linking systems through standard interfaces and formats

For ocean accounting, semantic interoperability is particularly challenging as statistical and geospatial communities have "evolved their own general data models, metadata capabilities, architectures, and data infrastructures, creating differences in fundamental terminology over time"^[14].

The Open Geospatial Consortium (OGC) provides the principal geospatial interoperability standards relevant to ocean accounting^[15]. Key standards include:

• Web Feature Service (WFS) -- for vector data exchange, applicable to administrative boundaries, marine protected area polygons, and ecosystem extent units
• Web Map Service (WMS) -- for raster map images and tiles, useful for bathymetric visualisation and land cover products
• Geography Markup Language (GML) -- the XML encoding standard for geospatial features

Two additional ISO standards are essential for geospatial metadata management. ISO 19115 (Geographic information -- Metadata) provides the standard schema for describing geographic datasets and services, defining the metadata elements needed for data discovery, evaluation, and use^[16]. Its XML implementation, ISO/TS 19139, specifies the encoding format in which ISO 19115 metadata should be stored and exchanged, enabling machine-readable metadata interchange between systems^[17]. Together, these standards ensure that geospatial data used in ocean accounts carry sufficient provenance and quality information for informed integration.

The GSGF recommends that organisations "host appropriate technical infrastructures which support the use of the relevant standards where systems and services are linked through standard interfaces, services, and data formats"^[18].

Marine Domain Standards: IHO S-100

The International Hydrographic Organization (IHO) S-100 standard provides the Universal Hydrographic Data Model for marine geospatial data^[19]. S-100 Edition 5.2.0, adopted in June 2024, builds upon the earlier Edition 5.1.0 (October 2023) and establishes "a contemporary hydrographic geospatial data standard that can support a wide variety of hydrographic-related digital data sources, and is fully aligned with mainstream international geospatial standards, in particular the ISO 19100 series"^[20]. A significant milestone was reached in December 2024, when IHO Member States approved operational editions of key S-100-based Product Specifications, including S-101 (Electronic Navigational Charts), S-102 (Bathymetric Surface), S-104 (Water Level Information), and S-111 (Surface Currents)^[21].

S-100 is designed to support applications "that go beyond the scope of traditional hydrography -- for example, high-density bathymetry, seafloor classification, marine GIS"^[22]. The standard comprises multiple parts covering conceptual schema, feature catalogues, spatial schemas, metadata, portrayal, and encoding formats including ISO 8211, GML, and HDF5^[23].

Part 16 of S-100 specifically addresses the Interoperability Catalogue Model, which "enables interoperability between disparate technologies through the use of common interfaces"^[24]. For ocean accounts that integrate hydrographic data (bathymetry, tides, currents) with ecosystem and economic data, S-100 provides the interoperability framework for the marine domain component.

3.2 Exchange Formats

Standardised exchange formats enable data to be transmitted between systems in machine-readable form. The choice of format affects both the technical ease of data integration and the preservation of semantic content.

SDMX Transmission Formats

SDMX provides three transmission formats optimised for different use cases^[25]:

SDMX-ML (XML-based) provides the most complete expression of the SDMX information model, supporting all structural metadata artefacts. The Structure Specific Data format is "the sole XML format for data exchange" following the deprecation of legacy formats in SDMX 3.0^[26]. SDMX-ML is appropriate for formal data exchanges where complete metadata preservation is required.

SDMX-JSON provides a JSON representation that is "well-suited to web-based applications and modern programming environments"^[27]. The format supports all SDMX data and metadata types and is increasingly preferred for API-based data access.

SDMX-CSV provides a "simple columnar format" that offers accessibility for practitioners without specialised tools while still enabling conversion to other SDMX formats "without loss"^[28]. For ocean accounting implementation, CSV may serve as a practical intermediate format for data validation and review.

The choice between formats involves trade-offs between expressiveness (SDMX-ML), interoperability with web applications (SDMX-JSON), and accessibility for non-specialist users (SDMX-CSV).

Geospatial Formats

For geospatial ocean accounting data, several format standards are relevant:

GeoJSON and JSON-FG (OGC Features and Geometries JSON) provide JSON-based formats for geographic features that are "well-suited to web mapping applications"^[29]. These formats are appropriate for ecosystem extent boundaries, marine protected area polygons, and other vector features.

GML (Geography Markup Language) provides the comprehensive XML encoding for geographic information conforming to ISO 19136^[30]. GML is required for formal data exchange in contexts where complete geometry representation and coordinate reference system information must be preserved.

HDF5 (Hierarchical Data Format version 5) is adopted by IHO S-100 for gridded and imagery data^[31]. HDF5 is appropriate for bathymetric grids, ocean temperature fields, and other multi-dimensional environmental data.

GeoPackage provides an SQLite-based container format that can store both vector and raster data. GeoPackage is increasingly used for distributing compiled geospatial datasets.

3.3 Interoperability Protocols

Interoperability protocols define how systems communicate to discover, query, and retrieve data. Effective protocols enable automation of data collection workflows, reducing manual processing and improving timeliness.

SDMX RESTful Web Services

SDMX defines a RESTful web services API for querying data and structural metadata^[32]. The API enables:

• Structure queries -- retrieving Data Structure Definitions, codelists, concept schemes
• Data queries -- retrieving data observations filtered by dimension values
• Availability queries -- discovering what data exists for given parameters

The RESTful API design means queries are expressed through URL patterns, enabling integration with standard web tools. For example, a query for fisheries catch data might specify the dataflow, reference area, time period, and species codes through URL parameters.

The SDMX registry architecture supports "automated processing" through subscription and notification services^[33]. Compilers can subscribe to data updates and receive notifications when new data becomes available, enabling near-real-time data integration workflows.

OGC Web Services

The OGC web services architecture provides interoperability protocols for geospatial data:

WFS (Web Feature Service) enables querying and retrieval of geographic features. WFS supports spatial and attribute filtering, enabling retrieval of ecosystem assets within specified geographic bounds.

WMS (Web Map Service) provides rendered map images. While less useful for analytical purposes, WMS enables visualisation of spatial context in ocean accounting applications.

Sensor Observation Service (SOS) provides access to sensor data and observations^[34]. SOS is relevant for integrating ocean monitoring data (water quality, temperature, wave height) into ocean accounts.

The GSGF emphasises that technical infrastructure should support "standard interfaces, services, and data formats, including metadata standards"^[18:1]. Implementation of OGC services enables ocean accounting systems to integrate with national spatial data infrastructures.

FAIR Data Principles

The FAIR Principles (Findability, Accessibility, Interoperability, Reusability) provide guiding principles for scientific data management that are increasingly applied to statistical data^[35]. The GSGF explicitly incorporates FAIR principles, noting that the Interoperability principle "emphasises metadata" and that "(meta)data should be ready to be exchanged, interpreted and combined in a (semi)automated way"^[36].

For ocean accounting, FAIR principles translate to practical requirements:

• Findability -- data should be registered in searchable catalogues with persistent identifiers
• Accessibility -- data should be retrievable through standard protocols with clear access conditions
• Interoperability -- data should use standard formats, vocabularies, and include qualified references to other data
• Reusability -- data should have clear provenance and appropriate licenses

The FAIR principles align with quality dimensions in TG-0.7 Quality Assurance and should guide the design of ocean accounting data management systems.

3.4 Classification Concordances

Classification concordances (also called crosswalks or mapping tables) enable translation between different classification systems. For ocean accounting, concordances are essential for:

• Linking economic activity data (ISIC) with product data (CPC)
• Mapping national ecosystem classifications to international reference classifications
• Converting between historical and current classification versions
• Integrating data collected under different classification frameworks

The classification systems relevant to ocean accounting are described in TG-0.2 Overview of Relevant Statistical Standards.

Activity and Product Classifications

The relationship between ISIC (activity classification) and CPC (product classification) is fundamental to economic statistics. As the CPC documentation explains, "each subclass of the CPC consists of goods or services that are generally produced in a specific class or classes of the ISIC"^[37]. The CPC documentation includes explicit correspondences to ISIC, enabling ocean accounting compilers to link product flows with producing industries.

The CPC serves as a "central" classification providing "a framework for international comparison and promotes harmonization of various types of statistics related to goods and services"^[38]. The classification was "developed primarily to enhance harmonization among various fields of economic and related statistics and to strengthen the role of national accounts as an instrument for the coordination of economic statistics"^[39].

With the endorsement of ISIC Rev.5 and CPC Ver.3.0 by the UN Statistical Commission in March 2024, compilers should note that the division-level structure for ocean-relevant activities has been maintained, though with increased detail at lower classification levels^[40]. For ocean accounting, key concordance relationships include:

• ISIC Division 03 (Fishing and Aquaculture) corresponds to CPC Division 04 (Fish and Other Fishing Products)
• ISIC Division 50 (Water Transport) corresponds to CPC Divisions 65 and 67 (Water Transport and Port Services)

The UNSD has developed official correspondence tables for the ISIC Rev.4-to-Rev.5 and CPC Ver.2.1-to-Ver.3.0 transitions^[41]. These tables should be used when bridging data compiled under different classification versions. Compilers working with historical data collected under ISIC Rev.4 and CPC Ver.2.1 should apply these official correspondence tables rather than constructing ad hoc mappings, to ensure consistency and comparability across time.

Detailed guidance on ocean-relevant economic classifications is provided in TG-0.2 Overview of Relevant Statistical Standards.

Ecosystem Type Concordances

The IUCN Global Ecosystem Typology (GET) provides the reference classification for ecosystem types in SEEA Ecosystem Accounting^[42]. However, "countries may have their own national classification system of ecosystems (or ecological areas) that could be used for the extent accounts. In such cases, developing a bridge or concordance (often called a schema crosswalk in GIS) of this national classification system with the GET reference classification may facilitate comparability across countries"^[43].

For marine ecosystems specifically, concordances may be required between:

• National marine habitat classifications and GET Marine Realm types
• EUNIS habitat classification and GET Ecosystem Functional Groups
• National land cover classifications and GET transitional realm types (for coastal areas)

The SEEA Biophysical Modelling Guidelines note that the GET "represents a global typological framework that applies a process-based approach to ecosystem classification across the whole planet"^[44]. The hierarchical structure (Realms, Biomes, Ecosystem Functional Groups) provides natural aggregation levels for concordance development.

Guidance on ecosystem classification for ocean accounts is provided in TG-1.2 Marine Ecosystem Types and TG-3.3 Ecosystem Accounts.

Concordance Development Process

Developing classification concordances involves:

1. Structural comparison -- examining the hierarchical structure and level of detail in each classification
2. Conceptual alignment -- identifying where definitions align or diverge
3. Mapping relationships -- defining one-to-one, one-to-many, or many-to-many relationships
4. Documentation -- recording mapping rationale and any approximations
5. Validation -- testing concordances with actual data

The UNFC (United Nations Framework Classification for Resources) provides an example of formalised concordance development: "mapping schemes have been developed showing the link between the UNFC2009 and the SPE and CRIRSCO classifications" for mineral and petroleum resources^[45].

Temporal Concordances

Classification systems evolve over time through revision processes. ISIC has progressed through Revisions 3, 3.1, 4, and now 5; CPC through Versions 1.0, 1.1, 2.0, 2.1, and now 3.0. The UN Statistics Division maintains official correspondence tables between versions to enable time series compilation across classification changes.

For ocean accounting, temporal concordances are required when:

• Compiling time series that span classification revisions
• Integrating historical data collected under previous classifications
• Comparing international data collected under different version implementations

The ISIC documentation emphasises that "continuity, i.e., comparability between the revised and preceding versions of ISIC, has always been a major concern expressed by the Commission"^[46]. The official ISIC Rev.4-to-Rev.5 correspondence table, developed between March and October 2024 following the endorsement of ISIC Rev.5 explanatory notes, should be consulted when compiling time series that bridge these classification versions^[47]. Similarly, the CPC Ver.2.1-to-Ver.3.0 correspondence table is available through the UNSD classifications portal^[48].

3.5 Data Harmonisation Workflow

Implementing data harmonisation for ocean accounts requires a systematic workflow that transforms heterogeneous source data into consistent, integrated accounting tables. This section describes the compilation procedure and provides a worked example.

Compilation Procedure

The data harmonisation workflow consists of four sequential phases:

Phase 1: Classify -- Assign source data to standardised classifications. For ocean accounting, this involves:

• Mapping economic activity data to ISIC Rev.5 divisions and groups
• Mapping product flows to CPC Ver.3.0 categories
• Mapping ecosystem types to IUCN GET Ecosystem Functional Groups
• Mapping spatial units to the national spatial framework and coordinate reference system

Classification mappings should be documented in concordance tables that record the source classification, target classification, mapping type (1:1, 1:many, many:1, many:many), and any assumptions or approximations.

Phase 2: Map -- Transform source data values to common units and temporal reference periods. This includes:

• Converting spatial coordinates to the accounting area's coordinate reference system (typically WGS84 for horizontal coordinates)
• Aligning temporal references to accounting period start/end dates (calendar year, fiscal year)
• Converting physical units to standard measurement units (e.g., tonnes for biomass, hectares for extent, cubic metres for water volumes)
• Converting monetary values to constant prices using deflators where time series require comparability

Mapping transformations should preserve source values in metadata to enable quality checks and re-transformation if specifications change.

Phase 3: Validate -- Check integrated data for consistency, completeness, and plausibility. Validation includes:

• Range checks: values fall within plausible bounds (e.g., catch cannot exceed vessel capacity)
• Balance checks: supply equals use in flow accounts
• Spatial checks: geometries are valid, no gaps or overlaps in exhaustive spatial frameworks
• Temporal checks: time series are continuous, no unexplained breaks
• Cross-source confrontation: data from multiple sources describing the same phenomena are reconciled

Validation should generate exception reports that flag issues for resolution. The quality assurance framework in TG-0.7 Quality Assurance provides detailed validation procedures.

Phase 4: Integrate -- Compile validated data into accounting tables. Integration produces:

• Asset accounts combining physical extent, condition indicators, and monetary valuations
• Flow accounts recording extractions, emissions, and ecosystem service flows
• Supply-use tables linking ocean economy production and consumption
• Combined presentations integrating environmental, economic, and social dimensions (see TG-3.8 Combined Presentations)

Table 3.5.1: Data harmonisation workflow phases

Worked Example: Harmonising Fisheries Data Using SDMX

This synthetic example demonstrates the data harmonisation workflow for integrating fisheries catch data from multiple agencies into ocean accounts. The scenario: a national statistical office is compiling fisheries accounts and must integrate data from three sources with different reporting frameworks:

• Source A: National fisheries agency -- species-level catch data by fishing zone, reported quarterly in tonnes live weight
• Source B: Port authority -- landing declarations by vessel and port, reported monthly in various product forms (whole fish, fillets, processed)
• Source C: Customs administration -- seafood export data using national tariff codes, reported monthly in kilograms and monetary value (FOB)

Phase 1: Classify

The compiler establishes an SDMX Data Structure Definition (DSD) for fisheries flow accounts with the following dimensions:

• REF_AREA: Fishing zone (territorial waters, EEZ zones by bathymetry, high seas)
• TIME_PERIOD: Accounting period (annual, with quarterly detail available)
• SPECIES: Fish species using FAO ASFIS codes
• ACTIVITY: Producing industry using ISIC Rev.5 codes (Group 031: Marine fishing)
• PRODUCT: Output product using CPC Ver.3.0 codes (Division 04: Fish and other fishing products)

For each source, the compiler develops classification mappings:

• Source A reports species using national codes; concordance table maps to FAO ASFIS codes (mostly 1:1)
• Source B records product forms; concordance table maps to CPC Ver.3.0 with conversion factors to derive live weight equivalent (1:many mapping: one product code may correspond to multiple CPC classes depending on processing level)
• Source C uses HS 2022 tariff codes; official UNSD concordance table maps to CPC Ver.3.0 (many:many mapping requiring allocation based on supplementary information)

Phase 2: Map

Unit conversions and temporal alignment:

• Source A data are already in tonnes live weight; temporal alignment aggregates quarterly data to annual totals for stock accounts while preserving quarterly detail for seasonal analysis
• Source B landing weights require conversion to live weight equivalent using species-specific factors (e.g., fillets to whole fish factor 2.5 for snapper, 2.8 for tuna). Conversion factors documented in metadata
• Source C export data in kilograms converted to tonnes; monetary values (FOB) converted to national currency constant prices using fish price deflator

Spatial mapping:

• Source A fishing zones map to ocean accounting spatial framework using GIS overlay of zone boundaries
• Source B port locations geocoded using national address gazetteer; landings attributed to fishing zones using vessel logbook data on fishing grounds (where available) or pro-rata allocation based on Source A zone proportions for species
• Source C export port locations geocoded; spatial attribution to production zones inferred from Source A and B integration

Phase 3: Validate

Validation checks applied:

• Range check: No individual vessel catch exceeds vessel hold capacity (cross-reference with vessel register)
• Balance check: Total catch (Source A) equals total landings (Source B) plus estimated at-sea discards, within tolerance threshold of 5%
• Cross-source confrontation: Export volumes (Source C) reconciled with landing volumes (Source B) accounting for domestic consumption, processing losses, and stock changes

Discrepancies identified:

• Source A reports 15,200 tonnes tuna catch in EEZ; Source B records 14,100 tonnes landings; difference attributed to unreported landings (estimated 600 tonnes based on observer coverage) and at-sea discards (estimated 500 tonnes, 3.3% discard rate consistent with observer data)
• Source C reports 8,200 tonnes tuna exports; reconciles with Source B after accounting for 4,800 tonnes domestic consumption, 900 tonnes processing losses (filleting, canning), and 200 tonnes cold storage stock increase

Validation produces exception report documenting assumptions and highlighting areas requiring further investigation (unreported catch estimation method).

Phase 4: Integrate

Validated data compiled into SDMX-compliant fisheries flow account table with dimensions:

The DSD includes attributes documenting data sources, estimation methods, and quality flags. The compiled data structure enables:

• Dissemination through SDMX registry for automated retrieval by analysts
• Integration with economic accounts using ISIC and CPC concordances
• Comparison with international fisheries statistics using FAO ASFIS species codes
• Spatial analysis of catches by zone for marine spatial planning

The worked example illustrates how SDMX Data Structure Definitions provide the standardised framework for harmonising heterogeneous source data, enabling systematic quality checks, and producing interoperable outputs for ocean accounting.

4. Summary

Data harmonisation and interoperability represent enabling capabilities for ocean accounting, underpinning the integration of diverse data sources into coherent accounting frameworks. The key elements are:

• SDMX provides the statistical data exchange standard, with DSDs defining data structures, transmission formats enabling machine-readable exchange, and registry services supporting data discovery
• Geospatial standards from OGC and IHO S-100, supported by ISO 19115 metadata standards, enable integration of marine spatial data, with the GSGF providing the overarching framework for statistical-geospatial integration
• FAIR principles guide data management to maximise findability, accessibility, interoperability, and reusability
• Classification concordances enable translation between ISIC/CPC for economic data, between national and international ecosystem classifications, and across classification versions over time, with official UNSD correspondence tables available for the ISIC Rev.5 and CPC Ver.3.0 transitions
• Data harmonisation workflow provides systematic procedures for classifying, mapping, validating, and integrating heterogeneous source data into ocean accounts

Implementation of these standards requires institutional investment in technical infrastructure, staff capacity building, and sustained collaboration across organisational boundaries. The GSGF notes that "both the statistical and geospatial communities operate their own general data models, metadata capabilities, architectures, and data infrastructures" and that bridging these requires "greater incorporation of geospatial processes, standards, and best practices in the statistical business processes and data management systems"^[49].

For ocean accounting practitioners, a pragmatic approach involves:

1. Adopting SDMX-CSV as an accessible intermediate format while building toward full SDMX-ML/JSON implementation
2. Ensuring geospatial data conforms to OGC standards and carries ISO 19115-compliant metadata to enable integration with national spatial data infrastructures
3. Documenting all classification concordances used, including rationale and limitations, and using official UNSD correspondence tables for transitions between ISIC and CPC versions
4. Implementing FAIR principles progressively, prioritising persistent identifiers and standardised metadata
5. Establishing data harmonisation workflows with systematic quality checks at each phase

All ocean accounts benefit from harmonised data infrastructure. Asset accounts (TG-3.1 Asset Accounts) integrate physical and monetary data from diverse sources, requiring consistent classifications and units. Physical flow accounts (TG-2.6 Physical Flow Accounts) depend on concordances between economic activity and product classifications. Combined presentations (TG-3.8 Combined Presentations) integrate environmental, economic, and social data, relying on interoperable formats and metadata standards. Data harmonisation and interoperability are therefore foundational to the entire ocean accounting framework.

Guidance on specific data integration challenges for individual account types is provided in the relevant thematic Circulars: TG-4.1 Remote Sensing, TG-4.2 Survey Methods, TG-4.3 Administrative Data, TG-4.4 Citizen Science, and TG-4.5 Research Data.

5. Acknowledgements

This Circular has been approved for public circulation and comment by the GOAP Technical Experts Group in accordance with the Circular Publication Procedure.

Authors: [Names and affiliations]

Reviewers: [Names and affiliations]

6. References