Avoid These Common Errors with Preliminary Data in R01 Grants
As much as you try to walk the path of a perfectionist, even the most careful and efficient researcher is going to drop the ball—especially when it comes to your NIH R01.
It’s a beast of a project with a lot of moving parts, but there are ways to avoid making some of the costly mistakes that can keep you from getting a fundable score.
As a research grant consultant, one area where I see errors popping up is in the preliminary data:
Errors In How You Use Your Preliminary Data
The main error I see around the use of preliminary data by early career researchers is in the Significance section.
The first thing you need to do in this section is to establish the argument for why your research needs to be done in the first place.
As you’re establishing your argument for your main research question or overarching hypothesis, what you’re actually doing is walking your reviewers through your thinking about how you arrived at these elements.
You describe the overarching scientific problem you’re trying to solve, what we already know about the problem, and what we don’t know about the problem (aka the gap in knowledge).
The most efficient way to do this is to synthesize what is already in the literature. This means you’re drawing upon what others and even you have already published on the topic, so reviewers have a deeper insight into what is already known about the larger scientific problem you are trying to solve.
However, what I see sometimes is that you put too much preliminary data in that opening section where you’re trying to establish the scientific premise of your entire research project.
This issue is that when you take this route, everything feels shaky. The reason it feels unstable is because, at this point, you don’t have enough of a foundation for what is already known about the problem. You want everything before the gap in knowledge that you’re exploring to be relatively well-defined, so that the gap you’re trying to fill is very clear. But if everything surrounding your gap in knowledge is also unknown, it’s hard for you to make a strong argument that you’re chasing after the right gap.
It comes off a bit like, we know a little bit about this, and we know a little bit about that, and early evidence is showing this and that.
There’s no real solid ground there to stand on to get to the point to say, here’s the gap that we’re focusing on. It’s almost like you’re sending people off in a bunch of different directions. Leaving reviewers wondering exactly what is the gap you are focusing on with your NIH R01.
So my recommendation here is to ensure that you’ve made a solid, clear, and logical argument that is leading to an obvious and clear gap in knowledge.
Your preliminary data helps establish your overarching hypothesis about the gap in knowledge, but the rest of the scientific premise (aka what is known about the problem) should be fairly well established already.
Missing or Insufficient Preliminary Data
Generally speaking you need preliminary data to inform the development of your overarching hypothesis and each of your aims. What I often see is preliminary data to inform only one of the aims, and so reviewers are left wondering about the basis of the other aim(s). Sometimes it’s just a matter of how the preliminary data is presented (which is easily fixable), but sometimes the work just hasn’t been done.
Ignoring Your Approach Section
The final error I want to bring up is in terms of your use of preliminary data in your approach section that speaks to feasibility. This is about reminding reviewers that you have done this before, and you can draw on some of that in the approach section.
Two things I encounter when reviewing R01s is that early career researchers are overexplaining or doubling up on information they already included. An opposite but bigger error is not including enough information in the approach section around the feasibility components that are relevant to your preliminary data.
Are You Making These Errors?
These are the main preliminary data errors I witness in my work with early career researchers who are working on their first R01 or their resubmissions.
As you go back through and begin reviewing your significance section, specific aims, and approach section, identify which errors are sticking out in your proposal.
Then begin to make changes that will help your reviewers gain a clear picture of your big why and get on board with your how, so you can move one step closer to earning a fundable score.
If you found this helpful, I strongly encourage you to sign up for our free resource library. We have lots of tools and tutorials to help you write a stronger NIH grant that gets reviewers excited about the potential of your research idea. There are also lots of other tools in there to help you plan and prepare your next grant so that you are organized and ready to go.