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datacommons-client

Resolve place names into Data Commons DCIDs and fetch statistical observations.

21|2|Updated Dec 8, 2025
Search Tags:#knowledge graph#data commons#dcid resolution#statistical observations#pandas dataframe#time series query
One-click install
npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill datacommons-client-silverstein
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datacommons-client
Source: https://github.com/silverstein/claude-scientific-skills-desktop/tree/main/corpus/datacommons-client
Command: npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill datacommons-client-silverstein

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you retrieve and analyze public statistical information by converting place identifiers into Data Commons DCIDs and then fetching observations for variables over time.

Core Features & Use Cases

  • Resolve entity identifiers (names, coordinates, Wikidata IDs): Map “California”, “San Francisco”, or external IDs to Data Commons DCIDs so you can reliably query data.
  • Query statistical observations (time series and latest values): Pull population, unemployment, income, and other indicators using variable DCIDs and entity DCIDs or entity expressions for hierarchies.
  • Explore the knowledge graph (nodes and relationships): Discover properties, entity names, and geographic parent/child relationships to find what to query next.
  • Use Pandas-friendly outputs: Convert responses into records/DataFrames to support trend analysis, comparisons, and plotting.
  • Optionally filter by data source (facets): Restrict results to specific provenance domains for consistency.

Quick Start

Resolve a place name like California and fetch its latest population value as a DataFrame for analysis.

Frequently Asked Questions about datacommons-client

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I resolve a place name to a Data Commons DCID for statistical queries?▼

To resolve place names to Data Commons DCIDs, you map identifiers like "California" or external Wikidata IDs using the Data Commons Python API v2 resolve endpoints, yielding reliable entity references for querying demographic and economic observations.

Can I fetch time series observations from Data Commons into a Pandas dataframe?▼

Yes, you can fetch statistical time series observations into a Pandas dataframe by querying the Data Commons API v2 observation endpoints with variable and entity DCIDs, receiving Pandas-compatible records for trend analysis and plotting.

How do I filter statistical observations by data source in Data Commons?▼

You can filter statistical observations by data source in Data Commons by applying optional facet filtering, restricting query results to specific provenance domains to ensure data consistency across your retrieved time series.

Do I need a DC_API_KEY to query the Data Commons knowledge graph?▼

A DC_API_KEY is not strictly required but is supported for optional authentication when querying the Data Commons knowledge graph to explore node properties, entity names, and geographic parent or child relationships.

What is the best way to query demographic and economic data for geographic hierarchies?▼

The best way to query demographic and economic data for geographic hierarchies is using entity expressions with the Data Commons API v2 observation endpoints, allowing hierarchy-based selection to retrieve targeted statistical observations.

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