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Fieldnote Supply Shop: Settlement reconciliation assertions

Settlement reconciliation assertions for Fieldnote Supply Shop. The five identities this document set claims, each with the exact figure it must produce: net payable 661.01 USD, deposit 603.17 EUR and net tax due 88.88. It also records the size of the deliberate defect, 0.02 EUR.

json

application/json

1.5 KB
Document Set
ecommerce
Industry
retail
Source Kit
retail-commerce
Synthetic
true
As Of
2026-09-08
Identities
5

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

Specifications

Document Set
ecommerce
Industry
retail
Source Kit
retail-commerce
Synthetic
true
As Of
2026-09-08
Identities
5
Net Usd
661.01
Encoding
UTF-8

Testing contract

Reference control
Scenario
Drive a reconciliation test from this file and assert each identity against the other documents in the set.
Expected result
All five identities hold against the shipped files: the 41 settlement lines sum to 661.01, the payout reconciliation gives the same figure, and the single FX conversion gives 603.17 EUR. The knownDefect block predicts settlement-payout-fx-rounding-mismatch.csv being out by 0.02 EUR.

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: Settlement reconciliation assertions” is a deterministic Novus Examples fixture for Data import. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: UTF-8. 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("settlement-reconciliation.json") as f:
    data = json.load(f)
print(type(data), len(data))

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