Mining the neuroimaging literature

Automated analysis of the biomedical literature (literature-mining) offers a rich source of insights. However, such analysis requires collecting a large number of articles and extracting and processing their content. This task is often prohibitively difficult and time-consuming. Here, we provide tools to easily collect, process and annotate the biomedical literature. In particular,<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://neuroquery.github.io/pubget/">pubget</ext-link>is an efficient and reliable command-line tool for downloading articles in bulk from PubMed Central, extracting their contents and meta-data into convenient formats, and extracting and analyzing information such as stereotactic brain coordinates.<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://jeromedockes.github.io/labelbuddy/labelbuddy/current/">Labelbuddy</ext-link>is a lightweight local application for annotating text, which facilitates the extraction of complex information or the creation of ground-truth labels to validate automated information extraction methods. Further, we describe repositories where researchers can share their analysis code and their manual annotations in a format that facilitates re-use. These resources can help streamline text-mining and meta-science projects and make text-mining of the biomedical literature more accessible, effective, and reproducible. We describe a typical workflow based on these tools and illustrate it with several example projects.