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A data exchange format is a set of representation rules that lets different systems encode, send, read, and process structured information. JSON and CSV are familiar examples. But sharing a format does not, by itself, guarantee that two systems agree on what a field means: that requires shared definitions, metadata, or a separate specification.
What a data exchange format defines
A format describes how information is represented: the syntax or structure that a system can serialize and another can parse. Ecma International calls JSON “a lightweight, text-based, language-independent syntax for defining data interchange formats” in ECMA-404, 2nd edition (December 2017).
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That definition also shows the boundary of a format. ECMA-404 specifies what counts as valid JSON syntax; it does not say what an application’s values mean. Two systems might exchange a JSON field named date, for example, but still need an agreement about its expected representation and interpretation. The IETF JSON data-interchange specification, RFC 8259, discusses interoperability considerations, but applications still need shared semantics appropriate to their exchange.
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- JSON: A text-based format for structured data, including nested objects and arrays. Its syntax alone does not establish an application’s field meanings.
- CSV: A concise, human-readable way to represent tabular data. The W3C notes that CSV alone does not specify column types or constraints such as uniqueness.
- XML: A standardized representation option that may be suitable when the receiving systems and domain conventions support it.
- HDF5 and RDF serialization syntaxes: Other examples identified by W3C in guidance on selecting formats appropriate to intended or potential use.
These examples are not a universal ranking. W3C’s Data on the Web Best Practices recommends standardized, machine-readable formats suited to their use, rather than naming one format as best for every exchange.
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Why a format is not a complete data contract
A format answers questions such as how records, values, or nested structures are written. A data contract must also address what those values mean and which are acceptable. Depending on the exchange, that can include types, required fields, allowed values, relationships, and constraints.
CSV makes the distinction easy to see: a file can be syntactically valid while leaving unclear whether a column contains dates, identifiers, or numbers, and whether values must be unique. The W3C’s tabular data model and metadata vocabulary describe ways to add metadata and validation structures, as well as mappings to representations such as RDF or JSON. The metadata supplies information beyond the CSV’s basic rows and columns.
Format versus dataset metadata
A data exchange format encodes the contents being exchanged. Dataset metadata describes the dataset or service—such as what it covers, who provides it, or how it can be accessed. Catalog vocabularies such as DCAT operate at that descriptive layer; they do not replace JSON, CSV, or another format used to encode the data itself.
Communities use different dataset-description practices. W3C’s dataset-exchange use cases, dated 17 January 2019, discuss approaches including DCAT, CKAN schemas, schema.org, ISO 19115, DDI, and SDMX. The right metadata vocabulary depends on the community and purpose.
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How to choose a format for an exchange
- Identify the data’s shape. Decide whether you are exchanging tabular records, nested or hierarchical structures, or specialized scientific data.
- Define meaning and validation needs. Determine whether consumers need types, required fields, constraints, or explicit definitions for values. If the format does not express these sufficiently, pair it with a schema or metadata specification.
- Check interoperability. Confirm that the systems involved support the format and that any domain standard or community convention is followed.
- Balance inspection and processing. Consider how readily people can inspect the representation and how reliably intended systems can parse it.
- Plan for reuse and discovery. Provide dataset descriptions and documentation when consumers need to find, understand, or reuse the data.
W3C’s guidance on data on the Web supports choosing standardized, machine-readable formats in light of intended use. It does not establish a universal winner among JSON, CSV, XML, or other options; the choice depends on the data, consumers, semantics, validation needs, and domain conventions.
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