The rapid growth of scientific literature is driving the need for automated tools to efficiently extract, process, and analyze critical data. In chemistry, datasets documenting the synthesis of new chemical compounds typically consist of detailed preparation procedures, accompanied by characterization data to confirm the purity and structural integrity. Experimental sections have traditionally been written by humans, for humans, to facilitate replication and validation, as well as to allow verification of the work through visual inspection. In the age of digitization and automation, ongoing efforts are aimed at making natural language synthesis instructions machine-readable and -actionable, leveraging robotic technologies and enabling self-optimization. In today's data-driven chemistry landscape, innovations that generate or curate high-quality, structured datasets are as essential as traditional experimental advancements. Evaluating experimental data from research articles and supplemental information has become an increasingly time-consuming task for authors, reviewers, and editors alike. In 2004, Goodman et al. developed an applet to semi-automatically check various characterization data copied and pasted from manuscripts. Detailed tests were conducted on ten papers and a further survey was conducted on a one hundred randomly selected data paragraphs of fifty papers. It was concluded that "preliminary tests with this program demonstrate that refereed and published experimental data is highly accurate, but errors are still occasionally perpetuated". While tools such as the experimental data checker can help improve data quality, their focus on individual errors limits the ability to gain broader insights into error patterns. The following research takes a closer look at the nature of errors in experimental chemistry papers using a single metric: accurate mass measurements using high-resolution mass spectrometry (HRMS). Accurate mass measurements support the proposed molecular formula and can be used to distinguish elemental compositions with similar nominal masses. Gratifyingly, the recorded data can also be easily checked for internal consistency. It was anticipated that a high throughput review of the accurate mass measurement data of over 3000 SI files might reveal patterns not visible through random sampling. To achieve this goal, a Python script was developed to perform a large-scale analysis of the data by systematically addressing the following tasks: