Finally! Data on What Study Section Really Cares About
Six years ago, NIH revamped their scoring system asking reviewers to provide numbers ranging from 1 (best) to 9 (worst) assessing applications Environment, Investigator, Innovation, Approach, and Significance.
NIH has emphasized Innovation (insert jazz hands), leaving many a weary grant writer to feel a need to invent fabulous new techniques to take DNA out of things, put it back in, and take it back out another time to reassure study sections that the gene you are studying does the thing you thought it would. And if you can do that in a nano platform with a high throughput screen, all the better.
It takes only a brief gander at NIH’s instructions to authors to reinforce the need for extra technological bedazzlement. It’s right there in big letters.
“Highlight Significance and Innovation”
It turns out that strategy may not be all it’s cracked up to be. This week, PLoS One published a study by Eblen et al. evaluated over 70,000 applications looking at what metrics best-predicted funding success. Innovation and Significance were NOT the winners. Approach was.
Yes, that entirely unglamorous doing a project the right way, asking smart questions, and using robust design correlated far better with success than other metrics.
Several questions leap to mind including..Why didn’t NIH do this analysis earlier? It seems that they’ve been directing folks to the wrong area to emphasize. Either that or study sections are going rogue. And, here’s a vexing one, are we so precious that we all have to get 1’s, 2’s and 3’s for Investigators and Institutions? I don’t love statistics, but if everyone scores above average, doesn’t that mean we are all average or the space-time continuum is going to implode or something?
Read the paper. It’s pretty impressive and an excellent reason to slow down, think harder and make sure your study section is clear that not only is your question timely and relevant, but that you are doing it in a thoughtful and thorough manner.
Figure from PLoS One How Criterion Scores Predict the Overall Impact Score and Funding Outcomes for National Institutes of Health Peer-Reviewed Applications Matthew K. Eblen, Robin M. Wagner, Deepshikha RoyChowdhury, Katherine C. Patel, Katrina Pearson