  Publicly available data from open sources (e.g., United States Census Bureau
(Census), World Health Organization (WHO), Intergovernmental Panel on Climate
Change (IPCC)) are vital resources for policy makers, students and researchers
across different disciplines. Combining data from different sources requires
the user to reconcile the differences in schemas, formats, assumptions, and
more. This data wrangling is time consuming, tedious and needs to be repeated
by every user of the data. Our goal with Data Commons (DC) is to help make
public data accessible and useful to those who want to understand this data and
use it to solve societal challenges and opportunities. We do the data
processing and make the processed data widely available via standard schemas
and Cloud APIs. Data Commons is a distributed network of sites that publish
data in a common schema and interoperate using the Data Commons APIs. Data from
different Data Commons can be joined easily. The aggregate of these Data
Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can
then be searched over using Natural Language questions utilizing advances in
Large Language Models. This paper describes the architecture of Data Commons,
some of the major deployments and highlights directions for future work.
