Overview

rtransparency identifies and extracts indicators of transparency from the full text of published biomedical articles. It works on two inputs: plain TXT files (typically converted from PDFs) and PMC XML files (the JATS XML served by PubMed Central). For each indicator it returns whether the indicator was found and, when found, the sentence or statement that triggered the detection.

Indicator	What it captures	TXT	PMC XML
Conflicts of interest	A COI / competing-interests disclosure	`rt_coi`	`rt_coi_pmc`
Funding	A funding / financial-support statement	`rt_fund`	`rt_fund_pmc`
Protocol registration	Registration on a trial / review registry	`rt_register`	`rt_register_pmc`
Novelty	Claims of novelty (“for the first time”)	`rt_novelty`	`rt_novelty_pmc`
Replication	Replication / independent-validation components	`rt_replication`	`rt_replication_pmc`
Data sharing	Data deposited or made openly available	`rt_data_code`	`rt_data_code_pmc`
Code sharing	Source code / scripts made available	`rt_data_code`	`rt_data_code_pmc`
AI-use disclosure	A statement that generative AI was (or was not) used to prepare the manuscript	`rt_ai`	`rt_ai_pmc`

rt_all_pmc runs all eight detectors together in a single pass: COI, funding, registration, novelty, replication, data sharing, code sharing and AI-use disclosure. (rt_all covers the first five from TXT; data, code and AI also have standalone TXT detectors, rt_data_code and rt_ai, but are not part of the rt_all wrapper.)

AI-use disclosure is the newest indicator. Journals have asked authors to disclose any use of generative AI (ChatGPT and similar) in preparing a manuscript only since 2023, so rt_ai_pmc evaluates the indicator only for articles published in 2023 or later and returns NA for earlier ones.

The package and its validation are described in Serghiou et al., Assessment of transparency indicators across the biomedical literature: How open is open? (PLOS Biology, 2021, doi:10.1371/journal.pbio.3001107).

How detection works

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.

Usage: PMC XML

The package ships an example PMC XML file. We use it below; replace the path with your own file to analyze a different article.

xml_path <- system.file(
  "extdata", "PMID32171256-PMC7071725.xml", package = "rtransparency"
)

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

Usage: TXT files

To analyze a PDF, first convert it to TXT with rt_read_pdf (this needs the poppler pdftotext utility installed), then run the TXT detectors. The chunks below are illustrative and are not executed when the vignette is built.

pdf_path <- system.file(
  "extdata", "PMID32171256-PMC7071725.pdf", package = "rtransparency"
)
article <- rt_read_pdf(pdf_path)
writeLines(article, "article.txt")

rt_coi("article.txt")
rt_fund("article.txt")
rt_register("article.txt")
rt_data_code("article.txt")
rt_ai("article.txt")    # generative-AI-use disclosure
rt_all("article.txt")   # COI, funding, registration, novelty, replication

rt_ai is the plain-text counterpart of rt_ai_pmc. A text file carries no reliable publication date, so rt_ai applies no 2023 year gate (is_ai_pred is always TRUE or FALSE, never NA) and cannot confine the scan to back-matter sections the way the XML detector does. Restrict it to articles published in 2023 or later, and expect a slightly higher false-positive rate on papers that use AI as a research method.

Processing many articles

rt_all_pmc_dir() runs all eight indicators over an entire directory (or a vector of file paths) in one call, designed for corpus-scale analysis.

# Sequential, in memory
res <- rt_all_pmc_dir("path/to/xml", remove_ns = TRUE)

# Resumable and parallel: results are written to a CSV in chunks, a re-run skips
# files already recorded, and a malformed file yields an is_success = FALSE row
# instead of aborting the run.
future::plan("multisession")
res <- rt_all_pmc_dir(
  "path/to/xml", remove_ns = TRUE, output = "results.csv", parallel = TRUE
)

Summarizing a corpus

With one row per article, rt_summary() reports per-indicator prevalence with a Wilson confidence interval and a sensitivity/specificity-corrected (Rogan-Gladen) prevalence; rt_score() adds a per-article count of openness practices; and rt_plot() draws prevalence bars and yearly trends. The transparency-summary vignette covers this in depth.

data(rt_demo)            # a small simulated example shipped with the package
rt_summary(rt_demo)[, c("indicator", "percent", "adj_percent")]
#> # A tibble: 8 × 3
#>   indicator           percent adj_percent
#>   <chr>                 <dbl>       <dbl>
#> 1 is_coi_pred           70.4        70.8 
#> 2 is_fund_pred          79.6        79.4 
#> 3 is_register_pred      29.7        30.8 
#> 4 is_open_data          20.4        25.7 
#> 5 is_open_code           8.5         9.13
#> 6 is_novelty_pred       54.4        62.8 
#> 7 is_replication_pred    9.42        8.67
#> 8 is_ai_pred            25.2        NA

Introduction to rtransparency

Stylianos Serghiou

2026-06-25