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Fieldnote Learning Centre — Workflow and import guide

Workflow and import guide for Fieldnote Learning Centre. Documents 5 source tables, exact expected totals and supported versus manual workflows.

Data import

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# Fieldnote Learning Centre

Fictional course capacity, enrollment, completion and assessment records for LMS mappings and reporting.

All entities and transactions are fictional. Snapshot: 2026-09-08.

## Start with the linked model

1. Open operations.xlsx and review the Summary sheet. Source tables contain stable primary keys and declared references.
2. Change source values in the workbook to explore the calculated totals and editable chart.
3. Compare output values with expected-results.json. The JSON Schema checks types; primary-key uniqueness and joins also require the listed semantic checks.
4. Use invalid-records.json only in a local validation harness. Its three cases exercise duplicate keys, wrong types and an orphan reference.

## Included tables

| Table | Records | Primary key |
| --- | ---: | --- |
| courses | 3 | course_id |
| learners | 12 | learner_id |
| enrollments | 18 | enrollment_id |
| attendance | 36 | attendance_id |
| learning_materials | 3 | material_id |

## Expected totals

- Enrollments: 18.00 records. Count unique enrollment_id values in enrollments.
- Passed completions: 9.00 learners. Sum enrollments.passed over the included records.
- Available course seats: 30.00 seats. Sum courses.capacity over the included records.

## Platform mapping

primary-records.csv is a generic example table. It is not a native platform export. Where this kit includes a named profile, use that profile’s separate CSV and schema and review its compatibility level before uploading.

## Scope and assumptions

- Synthetic scores are data-processing examples. Course completion is not certification or evidence of actual training.

Specifications

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

Testing contract

Reference control

Scenario: Use workflow and import guide in the Fieldnote Learning Centre enrollment, completion, assessment, attendance, learning-materials workflow.
Expected result: Documents 5 source tables, exact expected totals and supported versus manual workflows.

What is a .md file?

Markdown (MD) is a lightweight plain-text markup language that uses simple punctuation conventions to denote headings, lists, links, emphasis, and code. It is designed to be readable as-is and to convert cleanly to HTML. It is widely used for documentation, READMEs, and content authoring.

How to use this file

Use an example Markdown file to test parsers and renderers, verify GitHub-Flavored Markdown extensions like tables and fenced code, and exercise HTML-conversion pipelines.

How to use this file for testing

“Fieldnote Learning Centre — Workflow and import guide” 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: MD · 1,664 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.

Document fixtures list their internal structure (pages, fields, tracked changes, embedded objects) in the spec table. Test extractors, converters, and OCR against that known structure, and compare searchable↔scanned or format-twin companions when present.

Code examples

import markdown  # pip install markdown

html = markdown.markdown(open("guide.md").read())
print(html[:200])

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