There are several challenges and limitations associated with analysing the chemical stripes visualizations and patent dataset. The patent dataset, while comprehensive, may not always be current or complete and seems to contain historical depositions that have been discontinued (potentially partially explaining some "blips" seen around 2007 and 2016), posing significant hurdles for accurate data (and trend) analysis. Extracting chemical information via image recognition or text mining from patents is challenging, since older patent documents are lower quality and are thus quite noisy and error prone . The dataset in PubChem is reliant on the information provided by depositors and is mapped to CIDs during deposition based on the data received. Often, chemicals mentioned in the introduction of patents are not actually used in the application, leading to potential misinterpretation of their relevance, while any chemicals that happen to appear in Markush structures defined for drug discovery purposes may be overrepresented. Chemicals can be mistakenly identified due to their appearance in unrelated contexts, such as being part of an inventor's name. Such an error, reported for the 1913 patent linked to PFOS (US-1257524-A), based on a figure in the earlier viewpoint, led to a suggestion to use a higher threshold in the cut-off applied to the scoring of name recognition in future applications. The two earliest PFOS patents currently in PubChem (as of 29 June 2024) are due to misrecognised names: US-1257524-A was invented by Adolf Pfoser, while US-2290705-A was invented by Wilhelm Pfost -the first genuine PFOS patent is 1953 (see Figure ). Additionally, recent patents for PFOS may, for example, reflect new treatment technology, not PFOS production or use. The tabular view of the patent data in PubChem allows a quick check of the linked patents, assisting with rapid verification (see Figure ); this can also be downloaded for offline use.