  The TREC Deep Learning (DL) Track studies ad hoc search in the large data
regime, meaning that a large set of human-labeled training data is available.
Results so far indicate that the best models with large data may be deep neural
networks. This paper supports the reuse of the TREC DL test collections in
three ways. First we describe the data sets in detail, documenting clearly and
in one place some details that are otherwise scattered in track guidelines,
overview papers and in our associated MS MARCO leaderboard pages. We intend
this description to make it easy for newcomers to use the TREC DL data. Second,
because there is some risk of iteration and selection bias when reusing a data
set, we describe the best practices for writing a paper using TREC DL data,
without overfitting. We provide some illustrative analysis. Finally we address
a number of issues around the TREC DL data, including an analysis of
reusability.
