Article parsing

PMC XML is parsed with xml2. The XML root is standardized to the <article> node (the package accepts the OAI-PMH, EFetch <pmc-articleset> and bare <article> shapes), the namespace is optionally stripped (remove_ns = TRUE), and the text is split into the sections where each indicator usually appears: acknowledgments, footnotes / author notes, the body, the methods, the abstract and supplementary material. TXT files are read whole and split into paragraphs.

Rule-based detection

Detection is rule-based and interpretable: each indicator is a curated set of regular expressions applied to the relevant sections, rather than a machine learning model. This keeps the output auditable (the matched statement is returned) and reproducible.

• Conflicts of interest. Detected from structured COI footnotes (fn-type = "conflict"), from section titles (“Conflicts of interest”, “Competing interests”, “Declaration of interest”, “Duality of interest”), and from a set of text patterns covering financial relationships, consulting, fees, board membership, patents and explicit “no competing interests” declarations. Honoraria-to-subjects and reference text are masked to reduce false positives.
• Funding. Detected from the XML <funding-group> element, from funding section titles, and from text patterns such as “supported by”, “funded by”, “grant from / number”, named funders and award types. Acknowledged funding is required to use explicit funding language (a funding verb tied to a funder), so a bare mention of an institution or the word “support” is not enough. No-funding declarations are excluded.
• Protocol registration. Detected from registry identifiers (ClinicalTrials.gov NCT, PROSPERO CRD, ISRCTN, ANZCTR ACTRN, DRKS, IRCT, UMIN, ChiCTR) and from registration phrasing in the methods or footnotes.
• Novelty and replication. Detected from claim patterns such as “for the first time”, “to our knowledge”, “novel finding” (novelty) and “replicate”, “independently validated”, “confirmatory cohort” (replication), with negation filters (“failed to replicate”).
• Data and code sharing. Detected by a native detector (.detect_data_code) built from public repository facts and curated benchmark statements: field-specific accession schemes (GEO GSE, SRA / BioProject PRJNA, PDB, ArrayExpress, dbGaP, ProteomeXchange, Dryad / Zenodo / figshare DOIs, …), repository URLs and names, deposit / availability / data-availability-statement language, and supplement and file-format signals. Crucially it distinguishes sharing (“data were deposited in GEO”) from reuse (“data were downloaded from GEO”) and excludes “available on request”. Code repositories (GitHub, GitLab, Bitbucket) only count as data when paired with a data noun, so a code-only GitHub link is not mistaken for data sharing.
• AI-use disclosure. Detected from a “Declaration of generative AI” type section title, and from text that names a generative-AI tool (ChatGPT, GPT-4, Copilot, Gemini, an LLM, …) in a manuscript-preparation context (“used ChatGPT to improve the readability”) or in an explicit negation (“no generative AI was used”). A negative lookahead keeps the tool sense of “large language model” out of the writing-object pattern, and AI used purely as a research method (not for writing) is not counted. Only evaluated for 2023 onward.

Languages

Conflict-of-interest and funding statements are detected not only in English but also in Spanish, Portuguese, French, German and Italian, using language-distinctive patterns matched on transliterated (accent-stripped) text. The German conflict-of-interest detection rate, for example, rose from 33% to 97% once these were added. The other indicators are English-only for now.

All indicators at once

rt_all_pmc returns all eight indicators in one call, together with the matched statement text, the publication year and article metadata.

all_indicators <- rt_all_pmc(xml_path, remove_ns = TRUE)

dplyr::glimpse(
  all_indicators[, c("pmid", "year", "is_coi_pred", "is_fund_pred",
                     "is_register_pred", "is_novelty_pred", "is_replication_pred",
                     "is_open_data", "is_open_code", "is_ai_pred")]
)
#> Rows: 1
#> Columns: 10
#> $ pmid                <chr> "32171256"
#> $ year                <int> 2020
#> $ is_coi_pred         <lgl> TRUE
#> $ is_fund_pred        <lgl> FALSE
#> $ is_register_pred    <lgl> FALSE
#> $ is_novelty_pred     <lgl> FALSE
#> $ is_replication_pred <lgl> FALSE
#> $ is_open_data        <lgl> TRUE
#> $ is_open_code        <lgl> FALSE
#> $ is_ai_pred          <lgl> NA

is_ai_pred is NA here because this example article predates 2023; for a 2023 or later article it would be TRUE or FALSE.

Individual indicators

coi <- rt_coi_pmc(xml_path, remove_ns = TRUE)
c(is_coi = coi$is_coi_pred, text = substr(coi$coi_text, 1, 120))
#>                                                                                                                     is_coi 
#>                                                                                                                     "TRUE" 
#>                                                                                                                       text 
#> "Competing interests In the past 36 months, J.D.W. received research support through the Collaboration for Research Integ"

fund <- rt_fund_pmc(xml_path, remove_ns = TRUE)
c(is_fund = fund$is_fund_pred, text = substr(fund$fund_text, 1, 120))
#> is_fund    text 
#> "FALSE"      ""

register <- rt_register_pmc(xml_path, remove_ns = TRUE)
register$is_register_pred
#> [1] FALSE

Data and code sharing

rt_all_pmc already reports is_open_data and is_open_code; rt_data_code_pmc is the focused view that also returns the matched statements. Detection is native and needs no external packages.

data_code <- rt_data_code_pmc(xml_path, remove_ns = TRUE)

dplyr::glimpse(
  data_code[, c("is_open_data", "open_data_statements",
                "is_open_code", "open_code_statements")]
)
#> Rows: 1
#> Columns: 4
#> $ is_open_data         <lgl> TRUE
#> $ open_data_statements <chr> "Availability of data and materialsData will be s…
#> $ is_open_code         <lgl> FALSE
#> $ open_code_statements <chr> ""

rt_all_pmc and rt_data_code_pmc also return open_data_links and open_code_links: the repository and accession URLs extracted from the statements, ready to pass to FAIR-assessment tooling such as rfair. Article metadata (title, journal, identifiers, dates) is available separately via rt_meta_pmc.

meta <- rt_meta_pmc(xml_path, remove_ns = TRUE)
dplyr::glimpse(meta[, c("pmid", "doi")])
#> Rows: 1
#> Columns: 2
#> $ pmid <chr> "32171256"
#> $ doi  <chr> "10.1186/s12874-020-0914-6"

AI-use disclosure

rt_ai_pmc reports the publication year, the year-gated prediction is_ai_pred (NA before 2023) and the matched text. The ai-disclosure vignette covers this indicator in depth.

ai <- rt_ai_pmc(xml_path, remove_ns = TRUE)
c(year = ai$year, is_ai = ai$is_ai_pred)
#>  year is_ai 
#>  2020    NA