ArticlePloS one2026
Enabling transparent research evaluation: A method for historical RCR retrieval using public NIH metadata.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The demand for improvement of research output evaluation by the international science community has been recently formulated as the Declaration on Research Assessment (DORA) by the American Society for Cell Biology. The Relative Citation Ratio (RCR), introduced by the National Institutes of Health (NIH) is a novel metric indicating the influence of a publication on its peer group - publications from the same research field, as determined by co-citation analysis. The RCR can be viewed for the actual month. While historical RCR data exists within the NIH database, it is not exposed in a way that allows direct or simple access for researchers. We present a method to reconstruct otherwise inaccessible RCR data. A Python-based approach was deployed to extract RCR data from the NIH database and to plot RCR-values for every time point since introduction of the database. This method demonstrates the feasibility of recovering historical bibliometric information and may contribute to more transparent and accountable use of metrics in academic evaluation - in line with the goals of DORA and open science initiatives.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.