Guided path
Compare Public Data Sources Before Asking AI
A short workflow for grounding a research question in open datasets before summarizing it.
Leave with one dataset URL, one queryable identifier, and a clearer prompt for analysis.
This path keeps AI grounded by collecting a source, an identifier, and a dataset before generating a summary.
Best for
- Researchers, students, and builders who want to ground AI summaries in inspectable public sources.
- Anyone turning a vague question into data, identifiers, and a repeatable query path.
Before you start
- A narrow research question with at least one place, entity, topic, or time boundary.
- A place to save source URLs, query links, and notes before asking an AI assistant.
What you leave with
- One source URL, one structured identifier, and one dataset or API entry point.
- A cleaner analysis prompt that names the source context and known limitations.
Common pitfalls
- Asking AI for a synthesis before checking whether the source covers the question.
- Copying facts without preserving identifiers, dates, and dataset provenance.
Follow-up moves
- Turn the source and identifier into a reusable query or saved browser bookmark.
- Add a second source only after the first one answers a concrete part of the question.
Route
- 01
- 02
Find a structured identifier
Use Wikidata Query Service to locate entities or relationships that can be queried.
Wikidata Query Service - 03
Add a measurement source
Use Data Commons or Open-Meteo when the question needs place-based statistics or time series.
Data Commons
Tools and sources in this path
OpenAlexOpen OpenAlex and start here: Search a topic and inspect concepts, works, and related authors.
Wikidata Query ServiceOpen Wikidata Query Service and start here: Open an example query before writing your own SPARQL.
Data CommonsOpen Data Commons and start here: Search a place or statistic and inspect the linked data page.