What I Learned from Analyzing Accurate Mass Data of 3000 Supporting Information Files

Area of Science:

• Chemistry
• Scientific Publishing
• Data Analysis

Background:

• Accurate mass data is crucial for verifying molecular formulas in scientific research.
• Supporting Information (SI) PDFs are commonly used to present supplementary experimental data.
• Ensuring the quality and consistency of published data is essential for scientific integrity.

Purpose of the Study:

• To develop and validate a Python script for high-throughput analysis of accurate mass data from scientific literature.
• To assess the internal consistency and compliance of accurate mass data in Supporting Information PDFs from *Organic Letters*.
• To identify common errors in accurate mass data reporting and provide recommendations for improvement.

Main Methods:

• A Python script was created to systematically extract and analyze quadruplets of molecular formula, measured ion, calculated mass, and found mass from SI PDFs.
• The script was tested on over 3000 SI PDFs from *Organic Letters*.
• Data extracted included molecular formula, measured ion ([M + Na]⁺), calculated mass, and found mass.

Main Results:

• Only 40% of analyzed SI files with readable accurate mass data demonstrated internal consistency.
• A significant portion of the data failed to comply with established scientific communication guidelines (e.g., *The ACS Guide to Scholarly Communication*).
• The analysis uncovered unexpected errors in the reporting of accurate mass data.

Conclusions:

• The systematic analysis revealed significant issues with the quality and consistency of accurate mass data in published scientific literature.
• The developed Python script provides a valuable tool for identifying data quality problems.
• Actionable advice is provided to enhance the accuracy and reliability of chemical data reporting in publications.