Google Dataset Search by the Numbers
This work addresses the need for dataset discovery for scientists, governments, and companies, but it is incremental as it builds on existing metadata extraction and analysis.
The paper tackles the problem of making datasets on the Web discoverable by analyzing Google's Dataset Search, which has grown from 500K to 30M datasets since 2016, providing a large and diverse snapshot of data.
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.