CZ Software Mentions: A large dataset of software mentions in the biomedical literature

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 4 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 ~97000 unique software entities,
covering 78% of all links. We link 185 000 of the mentions to a
repository, covering about 55% of all software-paper links. 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.