Data Commons
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 0 of its 30 references that are in the map. First posted on arXiv 8 Sep 2023.
Builds on
0 of 30 references.
No references here yet.
Built on by
Citing papers per year: 2023: 1; 2024: 4; 2025: 3; 2026: 1.
9 papers.
- 2023Benchmarking and Improving Text-to-SQL Generation under Ambiguitycited by 34
- 2024General Geospatial Inference with a Population Dynamics Foundation Modelcited by 18
- 2024Knowing When to Ask -- Bridging Large Language Models and Datacited by 4
- 2024Ranking over Regression for Bayesian Optimization and Molecule Selectioncited by 4
- 2024Community search signatures as foundation features for human-centered geospatial modelingcited by 2
- 2025PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmarkcited by 2
- 2025A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Informationcited by 0
- 2025Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoningcited by 0
- 2026Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluationcited by 0
- 2026Google Earth AI Geospatial Reasoning Agent: Capabilities and a Systematic Evaluation11 Oct 2026
- 2025Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning22 Jul 2025
- 2025PhreshPhish: A Real-World, High-Quality, Large-Scale Phishing Website Dataset and Benchmark14 Jul 2025
- 2025A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information1 Mar 2025
- 2024General Geospatial Inference with a Population Dynamics Foundation Model11 Nov 2024
- 2024Community search signatures as foundation features for human-centered geospatial modeling30 Oct 2024
- 2024Ranking over Regression for Bayesian Optimization and Molecule Selection11 Oct 2024
- 2024Knowing When to Ask -- Bridging Large Language Models and Data10 Sep 2024
- 2023Benchmarking and Improving Text-to-SQL Generation under Ambiguity20 Oct 2023