Today Morgan discusses really really boring scientific talk titles. Giving a great science talk begins with having a great title, that captivates the audience and motivates them to come to your talk. Don’t be afraid of giving your talk an interesting title! You will stand out, because everyone else will continue to use boring dry talk titles. Standing out is good. It gets you noticed. Morgan shares her favorite title for a talk, “Modeling biology with equations is like strapping a …. ” (you’ll have to watch the video to find out).

Over at Bio Careers there’s a rather long winded article titled “Do we need more scientists?”

Since it is late and it has been a long day, I must admit that I only skimmed it.

But it seems to make the argument that a bunch of chicken littles have been crying about the sky falling, because we’re going to have a shortage of scientists and engineers.
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I have an email list with some great people on it, who have been sending me some good questions that I’ll be answering here (are you on the list yet?  If not, you can join by filling your info on the upper left).

The first email I want to respond to  is not really a question, but a comment:

I feel that writing grant proposals is such a colossal
waste of time… and the ones that I have been forced to participate in,
I found to be borderline dishonest.

It is too bad that people have experiences like this.  But it is not totally surprising.

While I wouldn’t call grant writing my favorite activity, it doesn’t have to be an awful experience every time.

But, I do see that some people feel that to get grants they have to deceive.  If  I ever felt I had to resort to that I would find a different profession to retain my own sanity, rather than go down that road.

Any accomplishment based on deception is not an accomplishment.

It is possible to get grants without deception or trickery.  The same is true in any scientific endeavor – if you do great science, you don’t have to resort to any kind of deception or trickery.  But you do have to be effective in communicating how and why it is great.

For example, one of my graduate students defended his thesis last week.  Initially he gave a somewhat boring practice talk.

One problem was that he had a negative result for some work that consumed more than 8 months of his time.  That negativity infected his brain.  He thought since the result was negative, his reporting of it had to be a negative experience as well.  That people wouldn’t appreciate the negative result like they would a positive.

But the facts are the facts.  It is all how you talk about the facts that is important.  It is the story you tell with them which determines how your data will be received.

I like the story of Pet Rocks for this reason.  Pet rocks were basic garden rocks with eyes stuck on them, that came in a nice box with an instruction manual about how to take care of them as a pet.

They made Gary Dahl, their inventor, a millionaire.  But, you object, they were just rocks! Why would anyone buy one?!

Gary didn’t deceive anyone into buying them by claiming they were “magic levitating rocks”.  No, they were simply rocks viewed and packaged as pets.  Enough people liked that “view” of the rocks that they paid money for it.  Maybe you or I wouldn’t, but that isn’t the point.  The point is that he conveyed an effective story that reverberated with his audience about how to view his “rocks.”

The same is true for your science.  Negative data can be just a “rock” with no meaning or value.  Or it can be presented as a “pet rock” that has meaning and value to someone.

Taking this approach, I pointed out to my graduate student after the practice that, while the result was negative,  he could use that result to tell an interesting story about why  it was negative, and what that implies biologically.  That’s what I mean by the “pet rock” approach.

My student ended up taking that advice, transforming a negative experimental result into something worth talking about.  His talk was well received, and the committee thought he did a great job.  He is now a newly minted Dr.

In other words, he transformed his talk without any dishonesty about his results.

I think people use dishonesty because they feel they are cornered and think they have no other way “out.”  But there is always another way out, it is just a matter of figuring out what that is.  Sometimes that takes a while to figure out.  It isn’t necessarily easy.  But it is always the better way in the long run.
A great grant proposal doesn’t manipulate data or deceive.   But it does takes results – bad or good – and make it clear how they fit into a valuable and interesting research story.

In part I I discussed the importance of using rejection as a learning platform. Here in Part II, I expand on those thoughts.

Realize that it is all a matter of odds

Last spring the NIH announced its ARRA (American Reinvestment and Recovery Act) funding. For whatever crazy reason, I became involved in writing five independent funding proposals in a span of six weeks. For three of them I was PI, and the other two were led by colleagues.

I still haven’t recovered from that. It was traumatic, and I suggest that nobody try to do that. My health has not been quite right since then. However, there was one useful thing about it: I could see what kind of odds I was playing, with results accruing in a short time span.

In every case, acceptance or rejection of your paper or proposal is just an odds game. You can skew the odds and bias the die rolls, but you can’t control many important parameters, such as who actually does the reviewing. Maybe the person doing the reviewing was having a bad day. Or maybe they just aren’t interested in your work. Or, maybe they love your work. You can’t control that, though you can bias it.

Realizing this is one of the keys to dealing with rejection. If you see it as a turn at the lottery machine, you won’t feel as bad when you don’t win. Nobody expects to hit the jackpot at a casino on every try. And you shouldn’t expect it with grants or papers, either.

The long cycle of feedback is difficult

Unlike a casino, in the grant and paper game you don’t get feedback quickly. It can take months or more. In a casino, if you’re not lucky, you can move onto another machine or table, or just quit playing. It is not so easy with the grant and paper game.

The long time spans between submission and results are not conducive to reflection about what worked and what didn’t, or why. Often by the time the rejection (or acceptance) letter comes, the whole affair is distant in your mind.

It is hard to get actionable data on your success rates in a reasonable time span. You may have a string of a few successes or a few failures, but, especially with the failures, there’s not much data there. Let’s say you’re submitting proposals to NIH and the odds of funding are 1 in 8. And let’s say you have three rejections in a row. You are still within the most likely outcome category (sad but true). But this string doesn’t tell you where you are in that category. Would it be better to submit to NSF? A foundation? Without hard numbers, any such decision is based more on gut feeling than anything else.

That’s why the “experiment” I performed of submitting five distinct proposals in a short timespan was so interesting. I am able to look at the results to see what happened.

Let’s focus on the three I submitted as PI (principle investigator). Two of them were funded and one was not. The one that wasn’t funded was submitted to a local consortium, and their rejection letter was just about as useless as the one I mentioned from PLoS Computational Biology. It just said that they had around 80 proposals submitted and were only selecting 6, and mine wasn’t one of them.

That was a hard rejection for two reasons. First, it contained no useful information. Second, it was a proposal to get my work on antibiotic resistance funded, for which I’ve had a lot of roadblocks.

On the other hand, with the two proposals that I did get funded, I am bringing in a lot of resources for my work and for my university. In this particular casino run, I won 2 out of 3 of the games. Not bad. I only had set out winning one out of five as my goal.

In the long run, it is that “two out of three” or “one out of five” that is far more important than the rejection of my antibiotic proposal.

Keep it in perspective

This is harder to see when you don’t have a track record to look back at. If it is your first proposal submitted as an independent scientist, and you get a rejection, it is very easy to feel like that may be the end of the world. At least that was the case for me.

But realize that every single successful scientist I know has had many, many rejections in their life. Even nobel prize winners. And often, the more leading edge the work, the greater the number of rejections.

So, I’ll distill this all down to two actionable items to keep in mind the next time that rejection notice comes your way:

1. Realize that you are playing in a casino and that you will always have rejection mixed in with acceptance. It is par for the course. Just like taking a turn at a slot machine, you shouldn’t feel bad if you don’t win on the first try.

2. Realize that you can bias the odds in your favor, which makes it far better than gambling. If I play the slot machine and loose, I can’t do anything to improve my odds on the next round. If I play the scientific casino and loose, I can almost always learn something from that loss which will improve my odds next time.

Therefore, rather than spending any energy feeling sorry for yourself (which I have done), spend that energy on figuring out how to improve your odds. Turn your next rejection into a learning lesson.

Addendum

After writing this and posting the first part, an editor from PLoS Computational Biology contacted me. I was nicely surprised to hear from them. They seemed concerned about my negative experience, and are looking into it. It is good to find that the journal cares about this. I understand that they have a lot of work to do, and that they must reject many papers. But as I discuss above, it is quite hard as an author to learn anything useful from a rejection letter like this, so I encourage them to make the rejections more informative in the future.

Or, dealing with rejection in your scientific career, Part I

This is part one of a two-part blog post.

There’s no doubt about it, rejection is hard to accept.

But when you choose a career in research, you will frequently encounter rejection.

It happened to me just the other day.

We submitted a paper describing a computational model of phenotype switching in the soil bacterium B. Subtilis to PLoS Computational Biology. The paper produced several new results that other models had not reported. It also revealed new biological insights into the mechanisms of phenotype switching. This journal requires that a submitted paper represent both computational and biological advances. Our paper seemed to meet that requirement.

The rejection letter included the following quote:

As with all papers submitted to the journal, yours was fully evaluated by myself in consultation with other members of the PLoS Computational Biology Editorial Team. While we appreciate the attention to an important topic, I regret that we do not feel that the manuscript provides the strength of the advance that we must require for PLoS Computational Biology.

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision and that you will consider PLoS Computational Biology for other submissions in the future.

It was not even sent out for review.

This is the second paper of mine that has been submitted to this journal recently, and been rejected without review.

In fact, I didn’t want to send the paper to the journal, but my co-author, a graduate student in my lab, really wanted to try this journal. I went along with her wishes.

This rejection letter is the least useful kind of rejection you can receive – because it contains no information about what was “wrong” with your paper.

I can only speculate what was wrong with mine.

But speculation does not help make it better.

Turning rejection into strength

As time has gone on, I’ve gotten better at dealing with the rejections.

One of the things that has made it easier is realizing that I can almost always use a rejection as a learning mechanism, that will improve my subsequent work.

The paper rejection I mentioned above is the worst type of rejection – one that comes with no useful information. Having had two such uninformative rejections from that journal in a short space of time means that I am unlikely to submit another paper to that journal.

I was able to turn this rejection into a strength in one way: learning that this journal is not a good place to submit my work.

Every time a rejection occurs, you should parlay it into a learning opportunity (I do). Some are more informative opportunities than others – but every one is an opportunity.

That’s the one “nice” thing about grant rejections from NIH or NSF: they almost always contain at least two reviews. From those, you may get at least some idea of where the reviewers were coming from, in order to figure out how to fix the problems (or whether to start over).

Grant or paper reviews don’t always tell you exactly what the deeper problems are with your work. So often you have to try to read between the lines. But the information is usually there if you look carefully.

I’ll expound on that thought on my email list (if you want to join, you can use the subscribe box in the upper left of my blog page).

And in part II of the article, I will discuss actionable items for dealing with rejection.