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Fieldnote Supply Shop: Tax reconciliation identities

Tax reconciliation identities for Fieldnote Supply Shop. 5 named identities across three operating models and two currencies, including the rounding-order identity that measures how far a per-row tax calculation drifts from a per-period one. The deliberate defect and its measured 100.00 USD shortfall are listed separately.

json

application/json

2.4 KB
Document Set
tax
Industry
retail
Source Kit
retail-commerce
Entity
Fieldnote Supply Shop
Synthetic
true
As Of
2026-09-08

Binary json: no in-browser preview. Download it above to open in a compatible application.

Specifications

Document Set
tax
Industry
retail
Source Kit
retail-commerce
Entity
Fieldnote Supply Shop
Synthetic
true
As Of
2026-09-08
Identities
5
Currencies
2
Defects Listed
1

Testing contract

Reference control
Scenario
Use this file as the expected-value table when testing a reader against the rest of the set.
Expected result
Every figure here is reproduced by at least two other files in the directory, and the deliberateDefects array names the single invalid file and the exact size of its shortfall.

What is a .json file?

JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.

How to use this file

Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.

How to use this file for testing

“Fieldnote Supply Shop: Tax reconciliation identities” is a deterministic Novus Examples fixture for Data import, JSON parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: JSON · 2,450 bytes. 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.

Code examples

import json

with open("tax-reconciliation.json") as f:
    data = json.load(f)
print(type(data), len(data))

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