9 – Follow the 3 ‘Ds’ for crystal-clear research objectives
In grant applications, objectives 1, 2, 3 should respectively be ‘Done’, ‘Doable’, ‘Dreamable’
Research objectives (also called ‘Specific Aims’) are the central component of your proposal. They’re the first contact between funding panels and your project's scientific logic, and should immediately come across as logical and achievable, even to non-experts.
However, funding panels and reviewers unconsciously expect a certain progression of your research objectives. If objectives don’t follow this progression, your project won’t get funded. Applicants learn this the hard way, over successive rejections, without ever getting a clear explanation. That's because this progression is part of unofficial funding rules.
As a result, myths about objectives abound. For example, researchers who receive a rejection notice stating that their second objective is too dependent on the first often start believing that objectives should be independent. In their next application, they submit a project in which objectives are independent… and are again rejected for submitting a ‘fragmented project’.
Here is where we see the progression unconsciously expected by funding panels and reviewers, which consists of 3D’s : Done, Doable, Dreamable. Being aware of this progression will help you design objectives that immediately come across as logical and feasible.
Let’s see the first ‘D’ for objective 1.
NB: this post deals with traditional funding schemes (i.e. not high-risk ones) and standard research projects (i.e. each objective depends on the previous one). I will deal with other types of funding schemes and projects in later posts.
1) DONE: Objective 1 should either already be 50-80% done, or inherently not risky
Most research funders are risk-averse – they don’t want to fund a project that might fail as early as objective 1. Therefore, you must reassure them that objective 1 is not risky, and that you will reach at least objective 2. There are two ways of providing this reassurance:
Let’s see each in turn.
1a) Present preliminary data that prove the feasibility of objective 1
The first solution consists in presenting an objective 1 that you have already partly done. This seems paradoxical, but today, the competition for funding is such that funders prefer to take as little risk as possible. They tend to fund projects in which objective 1 is 50-80% done.
Of course, never explicitly state that you’ve already done much of objective 1; simply present robust preliminary data (preferably based on your own work) that prove beyond doubt that it is feasible.
For example, imagine that objective 1 consists in interviewing 50 architects. You might write that:
- you have already located 25 architects willing to be interviewed;
- you have validated your interview questionnaire in a publication.
1b) Design an Objective 1 that is inherently not risky
The second solution consists in presenting an objective 1 that is inherently not risky. For example, in a successful grant application, I presented an objective 1 consisting of a bioinformatics analysis (in general much less risky than a real-world experiment). I explained that I had already performed and published a similar type of analysis, so funders knew that there was very little risk that something would go wrong.
“What should I do if objective 1 has some element of risk but no preliminary data to mitigate this risk?”
If at all possible, move the risky element downstream, to objective 2. If you can't, and you keep a risky objective 1, the hard truth is that you are highly unlikely to get funded. You have a few solutions:
- Apply for a proof-of-concept grant (see below).
- Gather preliminary data using some funding provided by your boss or colleagues, and apply when the data are ready.
“Isn’t the current funding model stupid?”
Yes, it is. But when you feel depressed, remember that there has never been a golden age where researchers were freely funded to do anything they wanted (or only in a few places, for a few years). Just think of Leibniz – this towering giant of science had to do countless chores for the Duke of Hanover in order to get funding for his research.
In my experience, you have 2 solutions for dealing with the current funding model:
- Play along with the system and try to become extremely efficient. This blog will help you do that and give you a competitive advantage, by teaching you outside-the-box tips derived from marketing, journalism and design and from my experience.
In parallel, to decrease your dependence on traditional funders, apply to funding schemes that support ideas and that don’t rely so much on preliminary data (e.g. proof-of-concept grants, or certain charities that support real, rather than incremental, innovation).
- Leave academia and continue research by funding yourself. That’s what I did (as a professional trainer and author, I can make a living by working part time, so I publish bioinformatics research on my spare time, as a rewarding hobby). A few researchers fund themselves by creating a start-up and becoming independently wealthy (not easy, but neither is chasing research funding for 30 years).
2) DOABLE: embarking on Objective 2 should be logical and reasonable
Objective 2 is the heart of your research project, for which funding panels are willing to pay. It should advance the state of the art, without being too big a leap – because most funders prefer incremental innovation, as we’ve seen in a previous post. Therefore, objective 2 should not yet have started, contrary to objective 1.
The right level of innovation: objective 2 is logical & reasonable
How do you judge whether objective 2 has the right level of innovation (not too incremental, not too risky)? Simple: on the basis of the preliminary data you present, panel members should think that is logical and reasonable to carry out objective 2. Therefore, if objective 2 relies on a new idea or concept, you should also present a proof of concept that supports it.
For example, imagine your project consists of mapping viruses found in jungle ants as a quick, inexpensive way of monitoring viruses from inaccessible parts of the jungle. A simple proof of concept would be showing that viruses found in garden ants reflect all viruses found in neighboring gardens.
Notice that contrary to objective 1, the main problem here is not technical feasibility (is it easy to monitor viruses in ants?), but conceptual feasibility (is it a good idea? Is it logical and reasonable to explore it?)
Now that you’ve convinced the funding panel that your project is feasible and logical, it’s time to inject a bit of ‘dream’ in it. That’s the role of Objective 3.
3) DREAMABLE: Objective 3 is a ‘cherry on top’, achievable in principle if everything goes smoothly
Objective 3 often serves mainly to inject a bit of dream (but in some projects it carries the actual payload, instead of objective 2)
Objective 3 is where you can inject a bit of ‘sexy’ in your project. I say a bit because if you promise too much, you won’t get funded. Presenting an overambitious project is the #1 mistake of junior (and not-so-junior) researchers. Funding panels know you cannot put an end to poverty or discover room-temperature levitation in 3 years with 300,000€…
Be careful not to be overambitious. Funders prefer a really good scientific project with a modest but achievable aim over a high-risk one with a giant impact. (Unless you’re applying to a high-risk funding scheme, of course).
Objective 3 should be feasible in principle, but can be a bit risky
Here’s a great guiding principle to ensure you gauge the risk of objective 3 correctly: it should be feasible if everything goes to plan.
I can hear you say “But it’s very rare that a 3-year research project proceeds as planned!” Exactly, and reviewers know that you might not be able to finish objective 3, and are fine with that… as long as you respect the other ‘Ds’, i.e. objective 1 is feasible and objective 2 logical and reasonable.
Objective 3 often (but not always) consists of an application of the theory, framework, prototype, material, knowledge... that you developed in objectives 1 and 2.
This being said, don’t feel you have to put in some sexy in objective 3. I cannot repeat it enough: funding panels get a lot more enthusiastic about a project where you make a scientific difference, even small, than about a sexy project artificially designed to deliver a hypothetical impact.
Summary
Here's a summary of the 3 'Ds' of objectives:
And that’s it! We’ve seen the 3 ‘Ds’ of objectives, a powerful methodology that is easy to apply and to remember, which is what this blog is all about.
While you’re at it, I highly recommend that you also read the chapter on designing clear objectives in my free guide Being clear without dumbing down.
Do your research objectives follow the 3 ‘Ds’?
--> Now your turn. Go and check your funding application: do your objectives follow the 3 ‘Ds’ (Done, Doable, Dreamable)? If not, you can move around the risky part of objective 1 to objective 2, or tone down objective 3 if it is too ambitious, etc.
Don’t worry as long as your objectives follow the logic behind the 3 ‘Ds’. For example, you may absolutely design a project whose actual payload is in objective 3 rather than in objective 2, as long as this payload is logical, reasonable, and not overambitious.
Or you may design a project in which objectives 1 and 2 are done in parallel, with objective 3 depending on both. In this case, neither objective 1 or 2 should be risky, to reassure reviewers that you will reach objective 3.
There are many other scenarios. This is why I am preparing a book on designing a clear scientific logic (in particular logical, memorable objectives), which is one of the 3 keys to getting funded.
Have a nice day and fruitful research.
David
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