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Willow Community Fund: Donor summary export JSON

Donor summary export JSON for Willow Community Fund. The same 8 supporters as donor-export.csv, with a privacy note recording that the export deliberately carries no contact or payment data. totals.deductible is 1669.50.

json

application/json

2.9 KB
Document Set
nonprofit
Industry
nonprofit
Source Kit
nonprofit-fundraising
Synthetic
true
As Of
2026-09-08
Rows
8

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

Specifications

Document Set
nonprofit
Industry
nonprofit
Source Kit
nonprofit-fundraising
Synthetic
true
As Of
2026-09-08
Rows
8
Contact Fields
0
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Parse the export, check the totals and check what personal data it actually contains.
Expected result
donors has 8 members whose amounts sum to the totals object exactly. No member carries an address, telephone number, email or payment identifier, so the file is a safe shape to test a donor import against.

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

“Willow Community Fund: Donor summary export 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: 8 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("donor-export.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.