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Fieldnote Learning Centre: Invoice register JSON

Invoice register JSON for Fieldnote Learning Centre. The same 18 invoices as invoice-register.csv with the four rules that govern them written out, including the capping rule that keeps net_payable non-negative. totals.netPayable is 4442.50.

json

application/json

9.3 KB
Document Set
education
Industry
education
Source Kit
education-training
Synthetic
true
As Of
2026-09-08
Rows
18

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

Specifications

Document Set
education
Industry
education
Source Kit
education-training
Synthetic
true
As Of
2026-09-08
Rows
18
Rules
4
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Test all four stated rules against every invoice in the array.
Expected result
All four rules hold on all 18 invoices: the fee components add up, the bursary subtracts, no net is negative and exactly 8 invoices are flagged for a plan. The array sums to the totals object at 4442.50 net.

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 Learning Centre: Invoice register JSON” 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: 18 rows · 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("invoice-register.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.