Starting Faculty Grants
Looking into various academic research departments, I’ve realized that the departments can look very different depending on where they are. This includes (and is certainly influenced) by NIH funding. Some places look like the investigators there can sometimes struggle to have consistent funding. Other places (at least seem like) they are rolling in money. The amount / type of science output seems to correlate accordingly.
I decided to actually quantitate this by identifying a few departments I was interested in, taking their relatively new faculty (largely associate and assistant professors, to keep the analyses relevant to the modern day), and seeing what their funding has looked like in NIH RePORTER. If plot out average investigator funding over the first 7 years (presumably the amount of time one requires to achieve “tenure”), this is what it looks like:
An aggregate of researchers I know across UW / Fred Hutch is included in the plot as the cyan set of points / lines. Notably, there are a fair number of investigators here that received the NIH New Innovator award (DP2) within the first few years, which shows up in the NIH RePORTER as a large lump sum, making the large bump in the early years of the curves. I hadn’t fully appreciated this before seeing this, but you really do get pretty good / successful scientists here.
I’m going to leave the other curves anonymous, but as you can tell, there are some pretty clear patterns. Green seems to have investigators come in with existing funding, but that funding doesn’t really seem to increase (in general) over the early career. While most investigators there do seem to get R01s, they don’t always have one consistently, averaging into the 120k per year range. The red curve doesn’t really start with NIH money, but everyone essentially gets consistent R01 funding over time. Purple also doesn’t generally start with NIH funding, but gets an R01 or equivalent (and more!) after the first few years (again, on average).
My usual caveat: Yes, the n values still aren’t great. And I’m sure this is biased; I largely included people I have met / know about, which is going to be a pretty biased subset (for being more successful). I tried to only include people that did pretty obvious biomedical work, so I wouldn’t have (the lack of awarded) NSF funding confounding the analysis. Yes, foundation grants aren’t taken into account here, though they won’t (generally) be as much as more typical NIH grants. Lastly, I avoided HHMI labs that would present a substantial amount of “invisible” money in this analysis (since not NIH), which also means I purposely left out some superstars (*ahem* Jay Shendure, Jesse Bloom…). Still, these curves look pretty consistent with what I had qualitatively interpreted based on various clues / indicators, so I think the overall patterns check out.