Data from: An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks

1. Systematic review, meta-analysis, and other forms of evidence synthesis
are critical to strengthen the evidence base concerning conservation
issues and to answer ecological and evolutionary questions. Synthesis lags
behind the pace of scientific publishing, however, due to time and
resource costs which partial automation of evidence synthesis tasks could
reduce. Additionally, current methods of retrieving evidence for synthesis
are susceptible to bias towards studies with which researchers are
familiar. In fields that lack standardized terminology encoded in an
ontology, including ecology and evolution, research teams can
unintentionally exclude articles from the review by omitting synonymous
phrases in their search terms. 2. To combat these problems, we developed a
quick, objective, reproducible method for generating search strategies
that uses text mining and keyword co-occurrence networks to identify the
most important terms for a review. The method reduces bias in search
strategy development because it does not rely on a predetermined set of
articles and can improve search recall by identifying synonymous terms
that research teams might otherwise omit. 3. When tested against the
search strategies used in published environmental systematic reviews, our
method performs as well as the published searches and retrieves
gold-standard hits that replicated versions of the original searches do
not. Because the method is quasi-automated, the amount of time required to
develop a search strategy, conduct searches, and assemble results is
reduced from approximately 17-34 hours to under 2 hours. 4. To facilitate
use of the method for environmental evidence synthesis, we implemented the
method in the R package litsearchr, which also contains a suite of
functions to improve efficiency of systematic reviews by automatically
deduplicating and assembling results from separate databases.