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Tern Client Services: Timesheet export JSON

Timesheet export JSON for Tern Client Services. The same 24 entries as timesheet-export.csv with a byProject rollup and a totals object. totals.hours is 72 and totals.billableAmount is 7200.00, and each byProject member names the invoice that billed it.

json

application/json

9.2 KB
Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
24

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

Specifications

Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
24
Projects
3
Encoding
UTF-8

Testing contract

Expected to pass
Scenario
Parse the export and assert both the byProject rollup and the totals against the entry array.
Expected result
entries has 24 members; grouping them by projectId reproduces the 3 byProject figures exactly, and summing either level gives 72 hours and 7200.00.

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

“Tern Client Services: Timesheet 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: 24 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("timesheet-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.