My colleagues within the NIH Office of Portfolio Analysis sought to answer this call. Drs. Ian Hutchins and George Santangelo embarked on a hefty bibliometric endeavor over the past several years to curate biomedical citation data. They aggregated over 420 million citation links from sources like Medline, PubMed Central, Entrez, CrossRef, and other unrestricted, open-access datasets. With this information in hand, we can now take a better glimpse into relationships between basic and applied research, into how a researchers’ works are cited, and into ways to make large-scale analyses of citation metrics easier and free.
Almost 11 years ago, Stefan Duchy, Benjamin Jones, and Brian Uzzi (all of Northwestern University) published an article in Science on “The Increasing Dominance of Team in Production of Knowledge.” They analyzed nearly 20 million papers published over 5 decades and 2.1 million patents and found that across all fields the number of authors per paper (or patent) steadily increased, that teams were coming to dominate individual efforts, and that teams produced more highly cited research.
Measuring the impact of NIH grants is an important input in our stewardship of research funding. One metric we can use to look at impact, discussed previously on this blog, is the relative citation ratio (or RCR). This measure – which NIH has made freely available through the iCite tool – aims to go further than just raw numbers of published research findings or citations, by quantifying the impact and influence of a research article both within the context of its research field and benchmarked against publications resulting from NIH R01 awards.
In light of our more recent posts on applications and resubmissions, we’d like to go a step further by looking at long-term bibliometric outcomes as a function of submission number. In other words, are there any observable trends in the impact of publications resulting from an NIH grant funded as an A0, versus those funded as an A1 or A2? And does that answer change when we take into account how much funding each grant received? ….
Many thanks for your terrific questions and comments to last month’s post, Research Commitment Index: A New Tool for Describing Grant Support. I’d like to use this opportunity to address a couple of key points brought up by a number of commenters; in later blogs, we’ll focus on other suggestions.
The two points I’d like to address here are: 1) why use log-transformed values when plotting output (annual weighted relative citation ratio, or annual RCR) against input (annual research commitment index, or annual RCI), and 2) what is meant by diminishing returns. ….
Last April we posted a blog on the measurement of citation metrics as a function of grant funding. We focused on a group of R01 grants and described the association of a “citation percentile” measure with funding. We noted evidence of “diminishing returns” – that is increased levels of funding were associated with decreasing increments of productivity – an observation that has been noted by others as well.
We were gratified by the many comments we received, through the blog and elsewhere. Furthermore, as I noted in a blog last month, our Office of Portfolio Analysis has released data on the “Relative Citation Ratio,” (or RCR) a robust field-normalized measure of citation influence of a single grant (and as I mentioned, a measure that is available to you for free).
In the follow-up analysis I’d like to share with you today, we focus on a cohort of 60,447 P01 and R01-equivalent grants (R01, R29, and R37) which were first funded between 1995 and 2009. Through the end of 2014, these grants yielded at least 654,607 papers. We calculated a “weighted RCR” value for each grant, ….