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Datasheets for datasets: a buyer’s template
Turn the academic datasheet idea into a practical diligence form. Blank fields become findings.
Updated 2026-10-27 · 7 min read
A datasheet explains a dataset’s origin, composition, preparation, intended uses and limitations. A manifest connects those statements to files. Neither is a substitute for the underlying evidence or the licence.
Gebru and colleagues proposed datasheets to improve communication between dataset creators and users. The downloadable Fiund planning template adapts that idea to a licensing review; it is not certification or an exact reproduction of the research questionnaire.
Record facts, estimates and unknowns separately
Describe motivation, source groups, time range, coverage, counts and counting units. State whether quantities are measured, estimated or vendor-reported. Do not turn missing fields into plausible-looking defaults.
Separate original sources from labels and transformations. Identify software, review status, exclusions and limitations. A clean-looking delivery can still be unrepresentative or missing essential evidence.
Add the commercial evidence map
Reference ownership and permission records, restrictions, proposed uses, recipients and source-specific conditions. Do not embed private identities or documents in a broadly distributed datasheet.
The actual licence governs use. Fiund standard buyer licences are non-exclusive and prohibit synthetic voice or likeness replication; a filled template does not expand those terms or grant a per-deal contributor veto.
Inspect the illustrative filled example
The linked demo includes a filled datasheet for generated test video and tones. It states that there are no recorded people and that no speech-quality, calibration or human-behavior claims follow. It is not anonymized customer data or a real inventory sample.
Use that example to inspect fields and file references, then request evidence for the actual collection. Synthetic structural examples cannot establish the coverage or permissions of a private archive.
Use the datasheet as a decision record
For every important requirement, record evidence requested, evidence supplied, reviewer, result and unresolved conditions. Tie the version to the evaluated manifest and files so later changes do not inherit an earlier approval silently.
Review representative samples, missingness, duplicates and source-aware splits before acceptance. A template should expose uncertainty and next actions, not make an unreviewed package look buyer-ready.
Frequently asked questions
Does this guide confirm a dataset is available?
No. Availability, permissions, included preparation and commercial terms require review of the actual proposed package.