Fieldnote Learning Centre: Gradebook with an impossible score and a missing one
Gradebook with an impossible score and a missing one for Fieldnote Learning Centre. The same 18 results as gradebook-export.csv with two cells changed. ENR-06 carries a score of 104 where the max_score column on the same row declares 100. ENR-12's score cell is empty while its passed column still reads true and its grade_band still reads distinction, so the file asserts a distinction it holds no score for.
csv
text/csv
- Document Set
- education
- Industry
- education
- Source Kit
- education-training
- Synthetic
- true
- As Of
- 2026-09-08
- Rows
- 18
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
- 18
- Max Score
- 100
- Out Of Range Score
- 104
- Out Of Range Enrolment
- ENR-06
- Missing Score Enrolment
- ENR-12
- Intentionally Invalid
- true
Testing contract
Expected to fail- Scenario
- Validate every score against the max_score on its own row, then re-derive the pass flag from the score column.
- Expected result
- ENR-06 fails the range check by 4 points against its own declared maximum of 100. ENR-12 fails the derivation: an empty score coerced to 0 gives a fail, which contradicts the passed and grade_band columns on the same row. A reader that coerces blanks to zero silently turns a distinction into a fail and reports 8 passes instead of 9.
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: Gradebook with an impossible score and a missing one” is a deterministic Novus Examples fixture for Data import, Error handling. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 18 rows. 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("gradebook-export-score-out-of-range.csv")
print(df.head())
print(df.dtypes)Related files
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Generated by generation/industry_documents.py. Free for any use, no attribution required, license.