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Cobalt Operations Team — Numeric expected results

Numeric expected results for Cobalt Operations Team. Exact table counts, metric formulas, 8 chart points and three negative-test outcomes. Paid hours=336.00 hours; Base pay=9,072.00 USD; Employees=8.00 records.

Data import
Preview, first 50 linesjson
{
  "schemaVersion": 1,
  "kitId": "business-people-operations",
  "asOf": "2026-09-08",
  "tableCounts": {
    "employees": 8,
    "shifts": 48,
    "leave_requests": 4,
    "service_slots": 24,
    "service_bookings": 18
  },
  "primaryKeyDuplicates": 0,
  "foreignKeyFailures": 0,
  "metrics": [
    {
      "id": "shifts-paid_hours",
      "label": "Paid hours",
      "value": 336.0,
      "unit": "hours",
      "calculation": "Sum shifts.paid_hours over the included records.",
      "formula": {
        "op": "sum",
        "table": "shifts",
        "column": "paid_hours"
      }
    },
    {
      "id": "shifts-base_pay",
      "label": "Base pay",
      "value": 9072.0,
      "unit": "USD",
      "calculation": "Sum shifts.base_pay over the included records.",
      "formula": {
        "op": "sum",
        "table": "shifts",
        "column": "base_pay"
      }
    },
    {
      "id": "employees-count",
      "label": "Employees",
      "value": 8.0,
      "unit": "records",
      "calculation": "Count unique employee_id values in employees.",
      "formula": {
        "op": "count",
        "table": "employees"
      }
    }
  ],
… 104 lines total: download for the full file.

Specifications

Kit
people-operations
Industry
people-operations
Schema Version
1
Synthetic
true
As Of
2026-09-08

Testing contract

Reference control
Scenario
Use numeric expected results in the Cobalt Operations Team staff-directory, scheduling, base-pay, appointments, capacity, leave workflow.
Expected result
Exact table counts, metric formulas, 8 chart points and three negative-test outcomes. Paid hours=336.00 hours; Base pay=9,072.00 USD; Employees=8.00 records.

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

“Cobalt Operations Team — Numeric expected results” 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: JSON · 2,148 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("expected-results.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

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