  We describe the CZ Software Mentions dataset, a new dataset of software
mentions in biomedical papers. Plain-text software mentions are extracted with
a trained SciBERT model from several sources: the NIH PubMed Central collection
and from papers provided by various publishers to the Chan Zuckerberg
Initiative. The dataset provides sources, context and metadata, and, for a
number of mentions, the disambiguated software entities and links. We extract
1.12 million unique string software mentions from 2.4 million papers in the NIH
PMC-OA Commercial subset, 481k unique mentions from the NIH PMC-OA
Non-Commercial subset (both gathered in October 2021) and 934k unique mentions
from 3 million papers in the Publishers' collection. There is variation in how
software is mentioned in papers and extracted by the NER algorithm. We propose
a clustering-based disambiguation algorithm to map plain-text software mentions
into distinct software entities and apply it on the NIH PubMed Central
Commercial collection. Through this methodology, we disambiguate 1.12 million
unique strings extracted by the NER model into 97600 unique software entities,
covering 78% of all software-paper links. We link 185000 of the mentions to a
repository, covering about 55% of all software-paper links. We describe in
detail the process of building the datasets, disambiguating and linking the
software mentions, as well as opportunities and challenges that come with a
dataset of this size. We make all data and code publicly available as a new
resource to help assess the impact of software (in particular scientific open
source projects) on science.
