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Fieldnote Learning Centre — Numeric expected results

Numeric expected results for Fieldnote Learning Centre. Exact table counts, metric formulas, 3 chart points and three negative-test outcomes. Enrollments=18.00 records; Passed completions=9.00 learners; Available course seats=30.00 seats.

Data import
Preview, first 50 linesjson
{
  "schemaVersion": 1,
  "kitId": "business-education-training",
  "asOf": "2026-09-08",
  "tableCounts": {
    "courses": 3,
    "learners": 12,
    "enrollments": 18,
    "attendance": 36,
    "learning_materials": 3
  },
  "primaryKeyDuplicates": 0,
  "foreignKeyFailures": 0,
  "metrics": [
    {
      "id": "enrollments-count",
      "label": "Enrollments",
      "value": 18.0,
      "unit": "records",
      "calculation": "Count unique enrollment_id values in enrollments.",
      "formula": {
        "op": "count",
        "table": "enrollments"
      }
    },
    {
      "id": "enrollments-passed",
      "label": "Passed completions",
      "value": 9.0,
      "unit": "learners",
      "calculation": "Sum enrollments.passed over the included records.",
      "formula": {
        "op": "sum",
        "table": "enrollments",
        "column": "passed"
      }
    },
    {
      "id": "courses-capacity",
      "label": "Available course seats",
      "value": 30.0,
      "unit": "seats",
      "calculation": "Sum courses.capacity over the included records.",
      "formula": {
        "op": "sum",
        "table": "courses",
        "column": "capacity"
      }
    }
  ],
… 86 lines total: download for the full file.

Specifications

Kit
education-training
Industry
education
Schema Version
1
Synthetic
true
As Of
2026-09-08

Testing contract

Reference control
Scenario
Use numeric expected results in the Fieldnote Learning Centre enrollment, completion, assessment, attendance, learning-materials workflow.
Expected result
Exact table counts, metric formulas, 3 chart points and three negative-test outcomes. Enrollments=18.00 records; Passed completions=9.00 learners; Available course seats=30.00 seats.

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 — 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 · 1,910 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.