Enabling transparent research evaluation: A method for historical RCR retrieval using public NIH metadata

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.