Data published by government agencies, intergovernmental organisations, and other authoritative sources is used by data analysts, software developers, and researchers alike on a daily basis. While each of the public datasets can be relatively easily accessed and used individually, most business and research questions can only be answered using a combination of multiple data sources after they have been "meaningfully" and "carefully" joined together.
Data Commons is an initiative that aims to clean, reconcile, and join publicly available datasets into a single open knowledge graph. Launched in May 2018, it has since grown to include the data from the United States Census Bureau, Eurostat, the Organisation for Economic Co-operation and Development, and many other sources.
An amalgamation of numerous public datasets, Data Commons itself is a dataset structured around a large collection of entities: places, people, organisations, and so on. All data flowing into the Data Commons graph is viewed as facts about those entities, making the resulting dataset a valuable source of "meaningfully" combined entity-oriented data.
The data vocabulary used to structure the Data Commons graph builds upon Schema.org, the most widely used vocabulary for structured data on the web, and is documented at schema.datacommons.org.
The Data Commons knowledge graph is a source of demographics, housing, education, and other types of data which can be used to power a range of tools and visualisations. Notable users of the Data Commons data already include Google Search and the Common Knowledge Project.
As an RDF-style knowledge graph, Data Commons can be queried using SPARQL. For example, the following query returns the names of three U.S. States with their DCIDs:
The Data Commons Graph can also be accessed programmatically using Python helper libraries or in Google Sheets via the Data Commons add-on.
Data Commons compiles and joins thousands of open datasets into a single knowledge graph which provides a unified view across all data sources, making the data more accessible and readily available for research and analysis.