For nearly 10 years, more women than men received PhDs in the biomedical sciences, yet women are still underrepresented at every subsequent stage of academic advancement. In 2015, for example, women earned 53% of PhDs, but they comprised only 48% of post-doctoral fellows, 44% of assistant professors, and 35% of professors. To better understand what might be contributing to women’s underrepresentation in later stages of academia, Dr. Lisa Hechtman and her colleagues at the National Institute of General Medical Sciences (NIGMS) analyzed “funding longevity by gender” among funded NIH investigators. Their analysis, recently published in the Proceedings of the National Academy of Sciences, yielded a number of interesting findings which I’d like to share with you. Continue reading →
In May 2016, we posted a blog on “How Many Researchers” NIH supports. We cited the findings of a University of Wisconsin workshop, which concluded that the biomedical research enterprise suffers from two core problems: too many scientists vying for too few dollars and too many post-docs seeking too few faculty positions. We also noted that NIH leadership and others were increasingly interested in describing the agency’s portfolio not only in terms of the numbers of awards and dollars (as we do each year in our “By the Numbers” reports), but also in terms of the numbers of researchers those awards support. Today we show updated figures on how many researchers are vying for NIH support and how many are successful. Continue reading →
We recently released our annual web reports, success rates and NIH Data Book with updated numbers for fiscal year 2017. Looking at data across both competing and non-competing awards, NIH supports approximately 2,500 organizations. In 2017 about 640 of these organizations received funding for competing Research Project Grants (RPGs) which involved over 11,000 principal investigators. Continue reading →
We previously referenced Ioannidis’ and Khoury’s “PQRST” mnemonic for describing research impact: “P” is productivity, “Q” is quality, “R” is reproducibility, “S” is sharing, and “T” is translation. We wrote several blogs about “P,” productivity, focusing on publications, citations, and more recently the relative citation ratio. Now we’ll focus on a different kind of “P” for productivity, namely patents (which arguably are also related to “T” for translation). …. Do NIH-supported papers that are cited by patents have a higher Relative Citation Ratio than those that are not cited by patents? As a refresher, the Relative Citation Ratio uses citation rates to measure the influence of a publication at the article level…. We identified 119,674 unique NIH grants that were funded between 1995 and 2007 and that generated at least one publication…. Continue reading →
By the 21st Century Cures Act, the Next Generation Researchers’ Initiative calls on the NIH to develop policies to increase funding opportunities for new researchers seeking to secure early independence. To put the Initiative in perspective and to extend on previous blogs we’ve posted on changing demographics in NIH-funded researchers, we thought it would be useful to explore trends according to career stage.
First, some definitions. We define “Early Stage Investigators” (ESI) as those who are within 10 years of completing their terminal degree or post-graduate clinical training and who have not yet secured independence as a PI of a substantial NIH research award. …. Continue reading →
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? …. Continue reading →
In a previous blog, we described the outcomes of grant applications according to the initial peer review score. Some of you have wondered about the peer review scores of amended (“A1”) applications. More specifically, some of you have asked about … Continue reading →
While NIH policies focus on early stage investigators, we also recognize that it is in our interest to make sure that we continue to support outstanding scientists at all stages of their career. Many of us have heard mid-career investigators express concerns about difficulties staying funded. In a 2016 blog post we looked at data to answer the frequent question, “Is it more difficult to renew a grant than to get one in the first place?” We found that new investigators going for their first competitive renewal had lower success rates than established investigators. More recently, my colleagues in OER’s Statistical Analysis and Reporting Branch and the National Heart Lung and Blood Institute approached the concerns of mid-career investigators in a different way – by looking at the association of funding with age. Today I’d like to highlight some of the NIH-wide findings, recently published in the PLOS ONE article, “Shifting Demographics among Research Project Grant Awardees at the National Heart, Lung, and Blood Institute (NHLBI)”. Using age as a proxy for career stage, the authors analyzed funding outcomes for three groups …. Continue reading →
“My first submission got an overall impact score of 30. Is that good enough? What’s the likelihood I’ll eventually get this funded?”, or, “My first submission was not even discussed. Now what? Does anyone with an undiscussed grant bother to … Continue reading →
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. …. Continue reading →