Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability.

Area of Science:

• Biomedical Science
• Computational Biology
• Scientific Communication

Background:

• Structured, computable data are essential for data-driven biomedical research.
• Automated text mining increasingly supports the creation of such data.
• Current text-mining tools are effective but not perfect, facing challenges with specific data types.

Purpose of the Study:

• To provide authors with practical guidance to enhance the computability of their research data.
• To address common difficulties encountered by text-mining tools in processing biomedical literature.
• To increase the discoverability and wider use of scientific research outputs.

Main Methods:

• Development of 10 straightforward writing guidelines for authors.
• Introduction of a web tool, PubReCheck, to assist authors.
• Focus on common cases that remain challenging for automated text-mining software.

Main Results:

• The proposed writing tips and web tool aim to improve data integration for text mining.
• Authors can proactively enhance their manuscripts for better machine readability.
• Anticipated outcome is increased accessibility and citation of research findings.

Conclusions:

• Adhering to these guidelines can significantly improve how research data is processed by text-mining tools.
• Enhanced data accessibility leads to broader impact and utility of scientific work.
• The PubReCheck tool and writing tips offer practical solutions for researchers in biomedical science.