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tsv3.2 KB

Fieldnote Supply Shop: Inventory feed as UTF-16 LE tab-separated text

Inventory feed as UTF-16 LE tab-separated text for Fieldnote Supply Shop. The same 12 variants, tab separated and encoded UTF-16 little endian with an FF FE byte order mark. Every ASCII character occupies two bytes, so a reader that opens the file as UTF-8 sees NUL between each letter and usually reports one column.

tsv

text/tab-separated-values

3.2 KB
Document Set
ecommerce
Industry
retail
Source Kit
retail-commerce
Synthetic
true
As Of
2026-09-08
Rows
12

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

Specifications

Document Set
ecommerce
Industry
retail
Source Kit
retail-commerce
Synthetic
true
As Of
2026-09-08
Rows
12
Encoding
UTF-16LE with BOM
Delimiter
tab
Bom Bytes
FF FE

Testing contract

Expected to pass
Scenario
Open the file as UTF-8 and then as UTF-16, and compare the column counts.
Expected result
As UTF-8 the header parses as a single unreadable column containing NUL bytes; as UTF-16 it parses as 14 tab-separated columns and 12 data rows identical to inventory-feed.csv.

What is a .tsv file?

TSV (Tab-Separated Values) is a plain-text tabular format like CSV but using tab characters as field delimiters. Because tabs rarely appear in data, it often needs less quoting than CSV. It is common in bioinformatics, logs, and command-line data workflows.

How to use this file

Use an example TSV to test tab-delimited parsing, header and column handling, and pipelines that ingest tabular data from Unix tools or scientific datasets.

How to use this file for testing

“Fieldnote Supply Shop: Inventory feed as UTF-16 LE tab-separated text” 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: 12 rows · UTF-16LE with BOM. 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("inventory-feed-utf16le.tsv", sep="\t")
print(df.head())

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