# Google Dataset Search by the Numbers

arXiv: https://arxiv.org/abs/2006.06894 · HTML: https://mlsanity.com/paper/2006.06894/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [Retrieval & Fine-Tuning for In-Context Tabular Models](https://mlsanity.com/paper/2406.05207/) (2024)
- [Generative Table Pre-training Empowers Models for Tabular Prediction](https://mlsanity.com/paper/2305.09696/) (2023)
- [TabLib: A Dataset of 627M Tables with Context](https://mlsanity.com/paper/2310.07875/) (2023)
- [In-Context Data Distillation with TabPFN](https://mlsanity.com/paper/2402.06971/) (2024)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data](https://mlsanity.com/paper/2302.06375/) (2023)
- [Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models](https://mlsanity.com/paper/2306.05052/) (2023)
- [TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation](https://mlsanity.com/paper/2502.16414/) (2025)
- [Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners](https://mlsanity.com/paper/2503.01256/) (2025)
- [Cracking Vector Search Indexes](https://mlsanity.com/paper/2503.01823/) (2025)
- [Responsible AI Challenges in End-to-end Machine Learning](https://mlsanity.com/paper/2101.05967/) (2021)
- [Generating Accurate Synthetic Survival Data by Conditioning on Outcomes](https://mlsanity.com/paper/2405.17333/) (2024)
- [Open Data on GitHub: Unlocking the Potential of AI](https://mlsanity.com/paper/2306.06191/) (2023)
- [Relationships are Complicated! An Analysis of Relationships Between Datasets on the Web](https://mlsanity.com/paper/2408.14636/) (2024)
- [Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes](https://mlsanity.com/paper/2509.09960/) (2025)
- [Do Data Agents Need Semantic Metadata? A Comparative Study in Agentic Data Retrieval](https://mlsanity.com/paper/2605.28787/) (2026)

## Abstract (arXiv)

Scientists, governments, and companies increasingly publish datasets on the Web. Google's Dataset Search extracts dataset metadata -- expressed using schema.org and similar vocabularies -- from Web pages in order to make datasets discoverable. Since we started the work on Dataset Search in 2016, the number of datasets described in schema.org has grown from about 500K to almost 30M. Thus, this corpus has become a valuable snapshot of data on the Web. To the best of our knowledge, this corpus is the largest and most diverse of its kind. We analyze this corpus and discuss where the datasets originate from, what topics they cover, which form they take, and what people searching for datasets are interested in. Based on this analysis, we identify gaps and possible future work to help make data more discoverable.