Mining the neuroimaging literature.

Area of Science:

• Biomedical Informatics
• Computational Biology
• Text Mining

Background:

• Automated analysis of biomedical literature (literature mining) is valuable but hindered by challenges in data collection and processing.
• Existing methods for collecting and annotating large volumes of scientific articles are often time-consuming and difficult to implement.

Purpose of the Study:

• To introduce a suite of tools designed to simplify the collection, processing, and annotation of biomedical literature.
• To enhance the accessibility, effectiveness, and reproducibility of text mining and meta-science projects in the biomedical domain.

Main Methods:

• Utilized pubget, a command-line tool for bulk downloading and processing articles from PubMed Central, including metadata and specific information like stereotactic brain coordinates.
• Employed Labelbuddy, a local application for text annotation, to facilitate complex information extraction and the creation of ground-truth labels for validating automated methods.
• Described repositories for sharing analysis code and manual annotations to promote reuse and collaboration.

Main Results:

• Demonstrated a streamlined workflow for biomedical literature analysis using the described tools.
• Successfully illustrated the application of these tools through several example projects, showcasing their practical utility.
• Provided a framework for researchers to more easily engage in literature mining and meta-science.

Conclusions:

• The developed tools significantly lower the barrier to entry for biomedical literature mining and meta-science.
• These resources promote more efficient, effective, and reproducible research by simplifying data handling and annotation.
• The described workflow and tools empower researchers to unlock deeper insights from the biomedical literature.