  Users of Web search engines reveal their information needs through queries
and clicks, making click logs a useful asset for information retrieval.
However, click logs have not been publicly released for academic use, because
they can be too revealing of personally or commercially sensitive information.
This paper describes a click data release related to the TREC Deep Learning
Track document corpus. After aggregation and filtering, including a k-anonymity
requirement, we find 1.4 million of the TREC DL URLs have 18 million
connections to 10 million distinct queries. Our dataset of these queries and
connections to TREC documents is of similar size to proprietary datasets used
in previous papers on query mining and ranking. We perform some preliminary
experiments using the click data to augment the TREC DL training data, offering
by comparison: 28x more queries, with 49x more connections to 4.4x more URLs in
the corpus. We present a description of the dataset's generation process,
characteristics, use in ranking and suggest other potential uses.
