We are most appreciative of the feedback we’ve received, through the blog and elsewhere, on NIH support of model organism research. In part 1 of this series, we mentioned that we asked two separate groups to analyze NIH applications and awards. In parts 1 and 2 we primarily focused on R01-based data that were curated and analyzed by our Office of Portfolio Analysis. In part 3, we show results from a broader range of research project grant (RPG) data that were prepared and analyzed by our Office of Research Information Systems. This group used an automated thesaurus-based text mining system which delves into not only public data such as project titles, abstracts, public health relevance statements, but also the specific aims contained in RPG applications. ….
We were pleased to hear the feedback on our previous post on NIH-funded model-organism research. One question a number of you asked is: what’s happening with research involving mouse models? Thanks to additional work by colleagues in NIH’s Office of Portfolio Analysis (OPA) and Office of Extramural Research/Office of Research Information Systems, I’m excited to … Continue reading “A Look at NIH Support for Model Organisms, Part Two”
In the spring, I attended my first NIH Regional Seminar on Program Funding and Grants Administration. I greatly enjoyed meeting with a diverse group of scientists and science administrators. During two “Open Mike” sessions, I had the opportunity to engage in rich conversations with attendees on topics I frequently include on this blog. The conversations were enjoyable and insightful – there’s nothing like discussing such important issues like funding trends, research accountability, and grants policy face-to-face with new and early career scientists. …. Why am I writing about a seminar that took place in May? Because I have some good news for those of you who didn’t get a chance to come to Baltimore! NIH is hosting a second Regional Seminar this year, from October 26-28 …..
In order to develop and implement data-driven policy, we need to carefully analyze our data to understand the “stories behind our metrics.” Without analyzing our data to know what’s going on, we’re essentially flying blind! A group of authors from the NIH Office of Extramural Research sought to investigate the stories behind peer review scoring and why some grant applications are more likely to be funded than others. They extended analyses previously reported by NIH’s Office of Extramural Research and National Institute of General Medical Studies. Last month, they published their analysis of over 123,000 competing R01 applications and described the correlations of individual component peer review scores – significance, investigator(s), innovation, approach, and environment – with subsequent overall impact score, and funding outcome. ….
Wangler, et al. recently published an article in Genetics on NIH funding for model organism research involving Drosophila. The authors extracted grant information from NIH ExPORTER and looked for the word “Drosophila” in either the title or abstract. By this approach the authors found that NIH support for Drosophila-based research is declining.
We chose to investigate further trends in NIH support for Drosophila and other model organism research. Two groups of NIH staff used two different approaches. Our Office of Research Information Systems (ORIS) used an automated thesaurus-based text mining system which mines not only project titles and abstracts but also the specific aims contained in the application; this is the system we use to generate “Research Condition and Disease Category” (or RCDC) tables, which are publicly posted to the NIH RePORT website. In a separate effort, our Office of Portfolio Analysis (OPA) supplemented a different text mining algorithm with extensive manual curation. Both methods – the wholly automated thesaurus-based text mining approach and the manual curation supplemented text mining approach – yielded similar findings. In this blog, we will present the results of the manually curated approach. ….
Back in January we talked about whether there is an advantage to working up to the last minute and submitting your grant application on the deadline. From the perspective of review outcomes, there was no advantage. In fact, applications submitted at the last minute seemed somewhat less likely to be discussed. Need another reason to apply early (by early we mean days, not hours, before a deadline)? We still see applications that fail ….
A few months ago, a researcher told me about his experiences with the relatively new NIH policy by which investigators are allowed to submit what we have come to call “virtual A2s.” Under NIH’s previous single resubmission policy, if an investigator’s de novo R01 grant application (called an “A0”) was not funded, they had one chance to submit a revision (called an “A1”). If the A1 application was unsuccessful, the applicant was required to make significant changes in the application compared to the previous submissions. NIH took measures to turn away subsequent submissions that were materially similar to the unfunded A1. Under NIH’s current policy, investigators may resubmit a materially similar application as a new submission after the A1 submission. We will call these applications “virtual A2s.” The researcher told me that his virtual A2 did not fare well; although his A0 and A1 had received good scores (though not good enough for funding), the virtual A2 was not discussed. He wondered, just how likely is it for a virtual A2 to be successful? ….
We were pleased to see the interest in our recent blog on the unique number of investigators applying for and receiving NIH research project grants (RPGs). Some of you (through the blog page or through other media) have asked about whether we have similar data for our Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) program. We have generated analogous figures for SBIR and STTR grants, and today’s post shares this investigation of the question, “How many unique researchers are seeking SBIR/STTR funding?” ….
Last year, Kimble et al. published the findings of a workshop held at the University of Wisconsin on proposed strategies to rescue biomedical research in the US. The workshop organizers brought to together a number of stakeholders, who concluded that the research community faces two chief problems: “Too many researchers vying for too few dollars; too many postdocs competing for too few faculty positions.” These conclusions raises at least two questions: How many scientists, or more specifically how many principal investigators, does NIH fund? And how many scientists (more specifically aspiring principal investigators) want to receive NIH funding? Today I’d like to discuss two ways to examine these questions, by looking at the number of principal investigators awarded funding, not just on a yearly basis, but also in a way that captures a broader view of NIH-supported scientists over a window of time. ….
In one of my earlier blog posts, I described an analysis looking at whether attempts at renewal are successful. We looked at data from fiscal years 2013-2015, and found that renewal applications have higher success rates than new applications, and that this pattern is true for both new and experienced investigators. In response to your comments and queries, we wanted to follow up on the analysis with some historical data that looks are whether success rates of competing renewals decreased disproportionately compared to new grant applications’ success rates. ….