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cbor623 B

CBOR: Concise Binary Object

The records as CBOR: the IETF concise binary object representation used in IoT and COSE/WebAuthn. For testing CBOR decoders and JSON↔CBOR conversion.

Preview: schema + first 5 rowscbor
idint64namestringemailstringdepartmentstringactiveboolscoredoublejoineddate
1001Ada Lovelaceada.lovelace@example.comEngineeringtrue98.52021-03-01
1002Alan Turingalan.turing@example.comResearchtrue952020-06-15
1003Grace Hoppergrace.hopper@example.comEngineeringfalse91.22019-11-20
1004Katherine Johnsonkatherine.johnson@example.comOperationstrue96.82022-01-10
1005Edsger Dijkstraedsger.dijkstra@example.comResearchfalse89.42018-09-05
Decoded table: all 5 rows shown.

Specifications

Rows
5
Columns
7
Format
CBOR (RFC 8949)
Model
document

Testing contract

Expected to pass
Scenario
Exercise CBOR: Concise Binary Object in its binary workflow. The records as CBOR: the IETF concise binary object representation used in IoT and COSE/WebAuthn.
Expected result
root type=dict; root item count=1; first keys/values=['employees']. Declared feature checks: columns=7; model=document.

What is a .cbor file?

CBOR (Concise Binary Object Representation, .cbor, RFC 8949) is a compact, self-describing binary data format modelled on JSON but extended with binary strings, tags, and streaming. It is used in IoT, COSE/WebAuthn, and constrained protocols.

How to use this file

Use an example .cbor file to test CBOR decoders, tag handling, and CBOR-to-JSON conversion.

How to use this file for testing

“CBOR: Concise Binary Object” is a deterministic Testaroo fixture for Conversion testing, Serialization testing. The same content exported across many formats and linked as a group, so you can convert one and diff against the expected twin.

Documented properties for this file: 5 rows · 7 columns · CBOR (RFC 8949). Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Generated by generation/data_binary.py. Free for any use, no attribution required, license.