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csv562 B

Tern Client Services: Professional fee schedule

Professional fee schedule for Tern Client Services. Five rate codes. STD at 100.00 per hour is the only billing rate any time entry uses, which is why every row of timesheet-export.csv carries 100.00; OOH and TRV are defined but unused in this period and carry used_this_period false. MIL at 0.62 per kilometre is the mileage rate mileage-log.csv applies and COST at 58.00 per hour is the internal rate project-profitability.csv uses.

csv

text/csv

562 B
Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
5

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

Specifications

Document Set
professional-services
Industry
professional-services
Source Kit
crm-services
Synthetic
true
As Of
2026-09-08
Rows
5
Rates Used
3
Encoding
UTF-8

Testing contract

Reference control
Scenario
Check every rate the other documents apply against this schedule.
Expected result
The three rates actually used all resolve here: 100.00 on all 24 timesheet rows, 0.62 on all 3 mileage rows, and 58.00 on all 3 profitability rows. The two rows marked used_this_period false appear nowhere else in the set.

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

“Tern Client Services: Professional fee schedule” 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: 5 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("fee-schedule.csv")
print(df.head())
print(df.dtypes)

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