Dataset Card Writer
by Khalid Al-Naimi · National Planning Council · v1.2
Prompt template
3 variablesGenerate a marketplace dataset card for a government dataset being published on the TASMU Cognitive AI Marketplace. Dataset name: {dataset_name} Schema summary: {schema_summary} Collection method: {collection_method} Sections: Overview (2–3 sentences), Intended uses, Out-of-scope uses, Collection and lineage, Known limitations and biases, Update cadence, Suggested citation. Write for a data scientist audience; state limitations honestly rather than promotionally.
Playground
Simulated sandboxExample input & output
Example input
- {dataset_name}
- Qatar Monthly Traffic Counts: Arterial Roads
- {schema_summary}
- station_id, road_name, direction, hour, vehicle_class (5 classes), count, quality_flag
- {collection_method}
- Automated inductive-loop and radar counting stations at 92 sites, aggregated hourly by the Ministry of Transport traffic data centre.
Example output
Overview: Hourly vehicle counts by class from 92 automated stations on Qatar's arterial road network, aggregated by the Ministry of Transport traffic data centre. Intended uses: congestion modelling, signal timing studies, infrastructure planning, and forecasting model training. Out-of-scope: individual vehicle tracking or enforcement, the data contains no plate-level records. Collection and lineage: inductive-loop and radar sensors with hourly aggregation; records failing sensor self-tests carry quality_flag = 'suspect'. Known limitations: three Al Khor stations were offline during February 2026 maintenance, and motorcycle counts are systematically undercounted by loop sensors (estimated 8–12%). Update cadence: monthly, within 10 working days of month end. Suggested citation: Ministry of Transport, Qatar Monthly Traffic Counts, Arterial Roads, via TASMU Cognitive AI Marketplace, 2026.
Publishing entity
- Author
- Khalid Al-Naimi
- Version
- v1.2
- Rating
- ★ 4.4
- Runs
- 890
- License
- Open
- Updated
- 2026-04-27
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fanar-gov-7b-instruct →This template is tuned for the sovereign model above. Forked copies keep the same target model binding until republished.
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