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Novus Examples
csv408 B

Fieldnote Learning Centre: Course catalogue with materials

Course catalogue with materials for Fieldnote Learning Centre. 3 courses with their fee, capacity, pass score and the exercise material attached to each. material_characters is the exact length of the markdown exercise held against the course in the operating model, from 197 to 253 characters, so a content-length assertion has a real number to check.

csv

text/csv

408 B
Document Set
education
Industry
education
Source Kit
education-training
Synthetic
true
As Of
2026-09-08
Rows
3

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

Specifications

Document Set
education
Industry
education
Source Kit
education-training
Synthetic
true
As Of
2026-09-08
Rows
3
Materials
3
Encoding
UTF-8

Testing contract

Reference control
Scenario
Import the catalogue and check the fee and the material length against their sources.
Expected result
All 3 total_fee values equal fee-schedule.csv and every material_id resolves to a markdown exercise whose character count equals the material_characters column exactly.

What is a .csv file?

CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.

How to use this file

Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.

How to use this file for testing

“Fieldnote Learning Centre: Course catalogue with materials” 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: 3 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 pandas as pd

df = pd.read_csv("course-catalogue.csv")
print(df.head())
print(df.dtypes)

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