rtransparency automatically identifies and extracts indicators of research transparency from the full text of biomedical articles, in both PubMed Central (PMC) JATS XML and plain-text (PDF-derived) form. Every prediction comes with the exact statement that triggered it, so results are auditable rather than a black box. Detection is rule-based (curated regular expressions over the relevant article sections), self-contained (no GitHub-only or AGPL dependencies), and ships with reproducible accuracy benchmarks.

The eight indicators

Conflicts of interest and AI disclosure are disclosure-based: a statement on the topic counts whether the disclosure is positive or negative. Conflict-of- interest and funding statements are detected not only in English but also in Spanish, Portuguese, French, German and Italian.

Installation

# From CRAN (when available)
install.packages("rtransparency")
# Development version from GitHub
# install.packages("remotes")
remotes::install_github("choxos/rtransparency", build_vignettes = TRUE)

No GitHub-only or AGPL dependencies are required; data and code detection is native (it no longer wraps oddpub). rt_read_pdf() (PDF to text) additionally needs the poppler pdftotext utility on your system. The optional furrr and future packages enable parallel corpus processing; ggplot2 enables plotting.

Quick start: all eight indicators in one call

library(rtransparency)
xml <- system.file("extdata", "PMID32171256-PMC7071725.xml", package = "rtransparency")
res <- rt_all_pmc(xml, remove_ns = TRUE)
# The predictions, one column per indicator:
res[, c("is_coi_pred", "is_fund_pred", "is_register_pred", "is_novelty_pred",
        "is_replication_pred", "is_open_data", "is_open_code", "is_ai_pred")]
# Each prediction is paired with the text that triggered it, e.g.:
res$coi_text
res$fund_text
res$open_data_statements

rt_all_pmc() returns one row with the eight predictions, the extracted statement for each, article identifiers and metadata, the year, and is_success. is_ai_pred is NA for articles published before 2023.

Per-indicator functions

Each indicator can be run on its own, for a PMC XML file or a plain-text file:

rt_coi_pmc(xml, remove_ns = TRUE)        # conflicts of interest
rt_fund_pmc(xml, remove_ns = TRUE)       # funding
rt_register_pmc(xml, remove_ns = TRUE)   # protocol registration
rt_novelty_pmc(xml, remove_ns = TRUE)    # novelty claims
rt_replication_pmc(xml, remove_ns = TRUE)# replication / external validation
rt_data_code_pmc(xml, remove_ns = TRUE)  # data AND code sharing (+ extracted links)
rt_ai_pmc(xml, remove_ns = TRUE)         # generative-AI-use disclosure (2023+)
rt_meta_pmc(xml, remove_ns = TRUE)       # article metadata

Corpus-scale processing

rt_all_pmc_dir() runs all eight indicators over an entire directory (or a vector of paths). It is built for large corpora:

res <- rt_all_pmc_dir(
  "path/to/xml",          # a directory, or a character vector of file paths
  remove_ns = TRUE,
  output    = "results.csv",  # resumable: re-running skips files already recorded
  parallel  = TRUE,           # via furrr + an active future::plan()
  progress  = TRUE
)

• Resumable: with output, results are written to a CSV in chunks; a re-run skips files already recorded and appends only the new ones.
• Failure-isolated: a malformed file yields an is_success = FALSE row instead of aborting the run.
• Parallel: set future::plan("multisession") and parallel = TRUE.

Plain-text input

The same detectors run on plain-text (PDF-derived) articles. rt_read_pdf() returns the extracted text as a character string; write it to a .txt file, then point the text detectors (which share the PMC detection logic) at that file:

article_txt <- rt_read_pdf("article.pdf")   # needs poppler's pdftotext; returns text
writeLines(article_txt, "article.txt")      # the detectors take a file path
rt_all("article.txt")                       # COI, funding, registration, novelty, replication
rt_coi("article.txt")                       # or one indicator at a time
rt_ai("article.txt")                        # generative-AI-use disclosure

rt_ai() is the plain-text counterpart of rt_ai_pmc(). Because a text file carries no reliable publication date, it applies no 2023 year gate (it returns TRUE/FALSE, never NA) and cannot confine the scan to back-matter sections, so restrict its use to 2023-or-later articles and expect a slightly higher false-positive rate on papers that use AI as a research method.

Summarizing a corpus

Once you have one row per article, summarize the corpus:

data(rt_demo)            # a small simulated example shipped with the package
rt_summary(rt_demo)      # per-indicator prevalence with a Wilson confidence
                         # interval and a sensitivity/specificity-corrected
                         # (Rogan-Gladen) prevalence
rt_summary(rt_demo, by = "year")   # subgroup summaries
rt_score(rt_demo)        # add a per-article count of openness practices met
rt_plot(rt_demo)                                  # prevalence bar chart
rt_plot(rt_demo, type = "trend", year = "year")   # prevalence over time

The accuracy correction uses the bundled rt_accuracy table (detector sensitivity and specificity for seven indicators). Supply your own estimates:

rt_accuracy                              # the bundled estimates
my_acc <- data.frame(variable = "is_open_data", sensitivity = 0.84, specificity = 0.97)
rt_summary(rt_demo, accuracy = my_acc)   # correct with your own values

Linking to FAIR assessment

The data- and code-availability links the detector extracts (open_data_links, open_code_links) can be passed to FAIR-assessment tooling such as rfair to score the findability and accessibility of the shared resources.

Validation

Benchmarked against the human-labeled XML benchmark of Serghiou et al. (2021), reproducible under data-raw/benchmark/, with results in inst/benchmark/:

Registration and code in the table above are labeled independently of the detector; COI, funding and data labels in the 1000-article 2023 sample were reconciled against detector-extracted statements (detector-adjudicated), so their agreement is not a fully independent estimate. Data sharing is deliberately precision-favoring: its 76.5% sensitivity trades recall for 99.0% specificity (the original oddpub algorithm scores about 84%/97% on this set).

The newer indicators are validated against maintainer-built, hand-labeled benchmarks in inst/benchmark/:

Replication's correction mixes designs (sensitivity from the enriched sample, specificity from the representative 2023 sample), so it is less clean than the single-design corrections above. AI-use disclosure is reported uncorrected and is excluded from rt_accuracy until a larger labeled post-2022 sample exists. Two further benchmarks live in inst/benchmark/: a five-language sample for multilingual COI and funding, and a TXT-parity benchmark comparing the text and XML detectors.

See vignette("rtransparency") for the methodology and vignette("scope-and-limitations") for what each indicator does and does not capture.

Documentation

• vignette("rtransparency") — introduction and methodology
• vignette("transparency-summary") — corpus prevalence, scoring and plotting
• vignette("ai-disclosure") — the AI-use disclosure indicator in depth
• vignette("scope-and-limitations") — indicator semantics, limitations, output schema
• Package website: https://choxos.github.io/rtransparency/

Lineage and citation

This package builds on the original rtransparent tool of Stylianos (Stelios) Serghiou, an enhanced, renamed fork maintained by Ahmad Sofi-Mahmudi (ORCID 0000-0001-6829-0823, GitHub @choxos). It adds four indicators (novelty, replication, AI disclosure, and a natively re-implemented data/code detector), multilingual COI and funding detection, plain-text parity, and corpus-scale batch processing. Serghiou is credited as an author.

The foundational paper: Serghiou et al., Assessment of transparency indicators across the biomedical literature: How open is open? PLOS Biology, 2021, doi:10.1371/journal.pbio.3001107. Run citation("rtransparency") for both references.

Getting help

Please file bugs or questions as issues at https://github.com/choxos/rtransparency/issues with a minimal reproducible example.