  Large-scale data sets on scholarly publications are the basis for a variety
of bibliometric analyses and natural language processing (NLP) applications.
Especially data sets derived from publication's full-text have recently gained
attention. While several such data sets already exist, we see key shortcomings
in terms of their domain and time coverage, citation network completeness, and
representation of full-text content. To address these points, we propose a new
version of the data set unarXive. We base our data processing pipeline and
output format on two existing data sets, and improve on each of them. Our
resulting data set comprises 1.9 M publications spanning multiple disciplines
and 32 years. It furthermore has a more complete citation network than its
predecessors and retains a richer representation of document structure as well
as non-textual publication content such as mathematical notation. In addition
to the data set, we provide ready-to-use training/test data for citation
recommendation and IMRaD classification. All data and source code is publicly
available at https://github.com/IllDepence/unarXive.
