The examples above illustrate how the various components of a scientific article can be prepared for AI/ML analysis by adding predictable structures to both the citations and the data implicit in the article. If this syntax looks rather long and unwieldy, it is because it is what is called an API (application programming interface) such as used by AI/ML applications (the specific API form of the above is ). It reveals all datasets derived from using the Gaussian quantum chemical application as restricted by the presence of an additional file containing further information (here the kinetic isotope eYects) and by specified title or description keywords, the search being within the global corpus of registered metadata. This extends the scope of the discovery well beyond that of a single ESI document. A way of constraining the search to a particular specified property, namely kinetic isotope eYects, would require future community agreement 18 on the vocabulary term and/or scheme to be used for that property. Here a possible such term is invoked by appending +AND+subjects.subjectScheme:*KIE*+AND+subjects.subject:1H/2H to the above search, which constrains the property to KIE and its value to 1H/2H (a hydrogendeuterium isotope eYect). The searches themselves can even be assigned a persistent identifier to facilitate discovery by e.g. AI/ML software. The community is here challenged to enable enrichment of the descriptive and relational publication metadata by agreeing wider vocabularies or search terms, thus enabling data discovery to be made ever more specific and accurate. 18