Dr. Strangefunding or: How I Learned to Stop Worrying and Love the Sequester

I’m currently sitting on a plane heading to Washington DC to give a presentation for the renewal of a large-ish project I’ve recently become involved with. This is significant in that, if it’s renewed, the project will give me additional funding through a critical time. Things are

wallpapers_wile-e-coyote_04_1280dire in research funding right now. For a digest of how dire see the following link. If you’re a research scientist who doesn’t know that I either pity you (because you’re so out of touch) or envy you (because you’re in a position where you just don’t have to worry). But I’m an optimist and optimists like to find the silver
lining of everything, or in this case the green lining (green as in cha-ching, $$$). So here are a collection of thoughts on why this whole sequester, with it’s accompanying 10% reduction in NIH funding, and corresponding decreases in other major funding agencies, just might be a good thing.

  1. The “bottom of the ditch” philosophy: This is my favorite. It goes like this. We’re at the bottom. Things are as bad as they’re likely to get. No, no, no, no, don’t tell me that they could get worse and explain why, I’m an optimist, I won’t believe you. So they’re bad, but we’re still in business and so long as we can scrape by for a little while things will turn around and pretty soon we’ll be rolling in the research dough again. Really. Our luck has to turn around soon, right?
  2. The “culling the herd” philosophy: What doesn’t kill us makes us stronger. Or, more to the point, what doesn’t kill ME makes me stronger- the rest of you all are on your own. And when we emerge from this bloodbath only the strong, fast, and very clever will survive to breed a better stronger generation of research scientists. So either figure out how to be the best or else find someone you can throw to the wolves and hungry lions while you make your escape.
  3. The “honing the elevator speech” philosophy: Previously, in the land of milk and honey and 25% funding levels for NIH R01 grants, you didn’t have to communicate effectively with non-scientists. You didn’t have to communicate all that well with scientists either. Now that we’re in a less favorable funding environment it’s a great time to start honing your outreach skills. Why on earth would ANYONE want to fund the esoteric basic science research that you’re proposing? You don’t just have to convince the reviewers by being really, really, really good at writing a good proposal story, but you also have to justify your existence to the broader public. You may also want to brush up your, “this is why I will make an excellent WalMart employee” elevator talk too, just in case.
  4. The “going back to school” philosophy: This is an excellent time to learn something new to expand your skills and research projects. Think of it as an opportunity. Diversification, without overextending, is really important in any business and is in research as well. Establish new collaborations, investigate new areas, learn new 7395256948_e355c85bc3_bthings- diversify. And you’ll be stronger for it. Will this translate into more $$$ for your research? Maybe not right away, but the investment will pay off.
  5. The “biology not bombs” philosophy: Do you know how much money is spent in this country on defense? It vastly dwarfs research spending by any measure. So one of the outcomes of the sequester is that defense spending will be cut, in a big way. You can debate on whether this is a good thing or bad thing in general, but it’s likely to have an eventual impact on our national priorities. I believe (you can disagree) that we will realize that this isn’t as big of a problem as everyone (or at least a big section of the country) thought it would be. We don’t need to spend a godzillion dollars on the next fighter jet (singular- you want more than one?) And that, in turn will eventually trickle down to a reevaluation of our other priorities. And eventually, it may realign our collective dedication to top-notch, sufficiently funded research in this country. As I said at the start, I’m an optimist.

Feel free to chime in with your optimistic views for research funding. I, for one, say bring on the sequester. I’m ready. I’m not afraid. To quote Major Kong: “Yeeeee-haw. YEEEEEEEEEEEEE-HAAWWWWWWW!!!!”

[2/26/13 9PM Updated RE Ron’s catch below. Yes the first bomb riding clip I had was dubbed in German. That’s what I get for posting from the plane.]

Finally, one last thought.

 

Personal relics: A German-made mandoline from my great aunt

Everyone has things around the house that have personal significance. I was thinking about this the other day and thinking it would be interesting to post some of these items along with something about the memories I associate them with- in a lot of cases centered around a person.

I love to use this mandoline. Not only is it great for slicing and julienne-ing, but it’s also just cool to look at. I’m not sure how old it is but the blade is still razor sharp (not just an DSC_4050expression- I’m pretty sure I could actually shave with it). I see a bunch of newer mandolines on-line with safety barriers to keep you from slicing your fingers off- not this baby. It’s caveat emptor with regard to your finger health. I don’t remember how I came by it, if it was given to me by my great aunt or if it was something I got after she died, but I value it.

My great aunt Hildegard was a force to be reckoned with. One family camping trip, after a late night around the campfire with the grown-ups imbibing and singing late into the night, she walked around to each sleeping site in turn with a wooden spoon and large pan, banging on it and telling everyone to get out of bed. At about 7 AM. I remember when I was very young her house was one that had strict rules about how we could sit on the couch and what we could touch. But she also told me that fairies were real- all I needed to do was be quick enough to look behind a tree, because they were very quick. She was a wonderful cook who had a large number of recipes that I remember- her Christmas cookies a standout. She made a wide variety and they were all so different from the standard American fare (coming from the Czech republic and Poland) and presented so beautifully. She made amazing cakes. And for all of her European properness and style, she was really a progressive and flexible thinker- much like her brother, my grandfather. After my great uncle Emil died (I was maybe a Sophomore in college) I went and stayed at her house for a night. She made me dinner and before dinner she asked me if I wanted a black russian telling me it had been a favorite of Emil. I thought that a black russian was something similar to Kahlua and cream- light. Not at all. That drink was possibly the first strong mixed drink I’d ever had and nearly did me in. I tried not to show it.

The last time I saw her was at my grandfather’s birthday in 2004. This was about a monthDSCN2041 before she died and I don’t even remember if I talked to her then. I know that after that party I thought about going to see her and then thought I should definitely give her a call on her birthday. I thought that I would have time to do that in the future. Of course I didn’t.DSCN0001

 

 

 

 

Instead, on a beautiful Saturday morning we headed with friends to downtown Seattle to visit the new library on it’s first day open to the public. We were having breakfast nearby and thought it would be nice to call my grandfather and see if he’d like us to take him there, maybe later in the day. I very, very rarely called my grandfather. This may haveDSCN2501 been the only time. He answered and politely declined and then told me that his sister had died the night before. The day before was her birthday and she had a great day with family. Apparently she was dropped off at her house where she lived alone after a great dinner, went into her bedroom and suffered a massive stroke. Given her strong will and desire to be in charge of her own fate it was, I’m guessing, as close to the way that she would have wanted to go out as possible.

So one way I remember my aunt is through her mandoline. Like her it is pragmatic, well-worn, and razor sharp.

Astrology: they might have something there

OK- that title was just to suck everyone in. I don’t believe in astrology any more than I believe in reading tea leaves to tell the future (I don’t at all, by the way). But here’s the weird thing: the month that you are born in has an influence on how long you live (statistically speaking). And that is very weird. Ptolemaicsystem-small There’ve been a number of studies on this and there’s probably more recent literature but I’ll talk about the results of a study published in PNAS in 2001, “Lifespan depends on month of birth” by Doblhammer and Vaupel.

In this study they examined birth records from Northern Hemisphere countries (Austria and Denmark) with a number of subjects over a million. They found that individuals born in autumn (October-December) lived, on average, about 4-8 months longer, than those born in spring (April-June). They then did the same comparison in the Southern Hemisphere and found that the relationship was opposite.

So it seems from their study that being born in fall months provides some protective benefit for overall lifespan. Why would that be? The authors conclude (from some further analysis) that events early in life might impact future health. These might be susceptibility to infectious and chronic diseases precipitated by differences in immune system development early in life. Since different seasons provide different environments for immune development (in the form of increased or decreased chances of infection, e.g.) this would seem to be a reasonable idea. The development of a healthy immune system may actually require more challenge, not less (which is why it’s thought that kids kept too clean have a higher chance of developing asthma and other autoimmune disorders)- which is why the winter months may offer more protection, eventually, for infants.

This study is a nice example of how seemingly nonsensical ideas, that the month you were born in can have an effect on your life, can sometimes have a grounding in real science. Of course, this has nothing to do with the alignments of the stars and planets, at least not in the mystical sense. Being born in December (in the Northern Hemisphere) I’m planning on enjoying my extra months somewhere sunny and tropical.

Twenty years of scientific publication

I just realized that this year marks the 20 year anniversary of my first scientific publication. Wow! That makes me about <mumble mumble> years old. But it’s a good point to reflect on my scientific genesis. The paper (I was middle author) was on biochemical characterization of HIV-1 matrix (MA) protein function, and I contributed by doing some of the work MatrixPaperFig1including showing that MA could be deleted and HIV would still be infectious. We also showed that the deletion-MA HIV packaged the envelope protein differently than the wild-type (naturally occurring) virus. According to Google Scholar the paper has been cited 89 times (here’s a post on how my other papers have fared) and to commemorate its 20th anniversary Journal of Virology has made it available for free on their website. (OK- maybe they just make things available for free after they get really old)

Here’s a timeline for my scientific genesis (the organized, formal part of it- I’ve always been a scientist at heart):

  1. 1989: Took a job for extra money in the Reed biology building supply room. Good move- knowledge and connections.
  2. 1990: Took an upper level bio course at Reed as a sophomore that was over my head (Animal Physiology with Dr. Steve Arch). Way over my head.
  3. 1991: Initiated an independent research project in Animal Physiology to examine the effects of cAMP treatment of Xenopus (frog) embryo neurite outgrowth with microscopy and horse radish peroxidase staining. More successful than it should have been.
  4. 1991: Applied for a summer research internship at Oregon Health Sciences University with Dr. Eric Barklis (see below). Got it somehow.
  5. 1991-1992 (summers): Learned molecular biology techniques, virology, and dipped my toes in how to actually do science.
  6. 1993: Did my Reed senior thesis project (on function of matrix protein) with Dr. Barklis through the year. Graduated from Reed.
  7. 1994-1995: Took a job as a research technician with same Dr. Barklis. Did work, published more papers.
  8. 1995: Accepted to OHSU Department of Microbiology and Immunology for graduate school. Got a pay raise (really) with my grad school stipend. Awesome!
  9. 1996: Chose, surprise, Dr. Barklis as my graduate mentor. Best decision ever.
  10. 2000: Finished grad school, defended my thesis, got my PhD! (that was waaaaaay easier to write than it was to do).
  11. 2000-2001: Did a short post-doc with Dr. Barklis while waiting for my future wife to finish school.
  12. 2001: Accepted a post-doc position with Dr. Ram Samudrala at University of Washington Department of Microbiology doing all computational work. Excellent decision.
  13. 2006: Accepted position at Pacific Northwest National Laboratory in Computational Biology and Bioninformatics. Awesome decision.

Cool example of invisible science

I recently posted on invisible science, unexpected observations that don’t fit the hypothesis and can be easily discarded or overlooked completely. Through a collaboration we just published a paper that demonstrates this concept very well. Here’s its story in a nutshell.

A few years back I published a paper in PLoS Pathogens that described the first use* of a machine learning approach to identify bacterial type III effectors from protein sequence. Type III effectors are proteins that are made by bacteria and exported through a complicated structure (the type III secretion apparatus- aka. the injectisome) directly in to a host cell. Inside the host cell these effectors interact with host proteins and networks to effect a change, one that is beneficial for the invading bacteria, and allow survival in an environment that’s not very hospitable for bacterial growth. Though there are a lot of these kinds of proteins known, there’s no pattern that has been found to specify secretion by type III mechanism. It’s a mystery still.

(* there was another paper published back-to-back with mine in PLoS Pathogens that reported the same thing. Additionally, two other papers were published subsequently in other journals that reiterated our findings. I wrote a review of this field here.)

So on the basis of the model that I published my collaborators (Drs. Heffron and Niemann) thought it would be cool to see if a consensus signal (an average of the different parts my model predicted to be important for secretion) that the model predicted would be hyper-secreted (i.e. would be secreted at a high level). I sent them a couple of predictions and some time later (maybe 8 months) Dr. Niemann contacted me to say that the consensus sequence was not, in fact, secreted. So it looked like the prediction wasn’t any good and that some work had been done to get this negative result.

But not so fast, because they’d had some issues with how they’d made the initial construct to do the experiment they remade the construct used to express the consensus. The first one (that was not secreted) used a native promoter and upstream gene sequence. This is the region that causes a gene to be expressed, then allows the ribosome to bind to the mRNA and start translation of the actual coding sequence. The native upstream sequence

Figure 1. Translocation of a consensus effector seq.

Figure 1. Translocation of a consensus effector seq.

was just taken from a real effector. When they redid the construct they used a non-native upstream sequence from a bacteriophage (a virus that infects bacteria), commonly used for expressing genes. All of the sudden, they got secretion from the same consensus sequence. This was a very weird result: why would changing the untranslated region suddenly change the function that the protein sequence was supposed to be directing?

The path of this experiment could have taken a very different turn here. Dr. Niemann could have simply ignored that ‘spurious’ result and decided that the native promoter was the right answer- the consensus sequence wasn’t secreted.

However, in this case the spurious result was the interesting one. Why did the bacteriophage upstream region construct get secreted? The only difference was in the upstream RNA (since the difference was in the non-coding region and the protein produced was exactly the same). Dr. Niemann pressed on and found that the RNA itself was directing secretion. And he found that there were other examples of native upstream sequences in the bacteria (Salmonella Typhimurium) that we were working on. This had never been observed before in Salmonella, though it was known for a few effectors from Yersinia pestis. He also identified an RNA-binding protein, hfq, that was required for this secretion. This paper is currently available as a preprint from the journal.

Niemann GS, Brown RN, Mushamiri IT, Nguyen NT, Taiwo R, Stufkens A, Smith RD, Adkins JN, McDermott JE, Heffron F. RNA Type III Secretion Signals that require Hfq. J Bacteriol. 2013 Feb 8. [Epub ahead of print]

So this story never ended in validation of my consensus sequence. Actually, in all likelihood it can’t direct secretion (the results in the paper show that, though it’s not highlighted). But the story turned out to be more interesting and more impactful and it shows why it’s good to be flexible in science. If you see the results of each experiment only in black and white (it supports or does not support my original hypothesis) this will be extremely limiting to the science you can accomplish.

 

Leading a collaborative scientific paper: My tips on cat herding

Large collaborative research projects, centers, or consortia have a single goal: to be funded for another round. That’s completely cynical, but it is not so far off the truth. The point of these projects is to advance science by bringing together many different experts in many different areas to do more than what could be done in a single R01-size endeavor. If there are no project-wide collaborative papers that come out of this effort going to high-profile journals there will be nothing- or very little- to make the claim that the project was successful. Why not just fund 3-8 R01-sized project that can work in isolation and accomplish the same thing or more? So publications are important.

The second thing to understand is that there’s no such thing as a ‘group-written’ paper, in my experience. Not truly. Someone always needs to step forward and take ownership of the paper to drive things forward otherwise it’s dead in the water. Maybe it can be two people, maybe it can be more- I’ve never seen it happen. So someone needs to step forward and be chief cat herder. This is a thankless job, but if it results in a solid, collaborative manuscript it can be very satisfying. Not to mention the fact that you will (or very much SHOULD) have your name first in the author order.

Here’s my metaphor for spearheading such a monster, errrr… paper.

Imagine that you’ve gathered a painter, a sculptor who works in clay, a sculptor who works with metal, and a DJ in a room- actually in many cases they’re not even in the same room, they’re distributed around the country in their own studios. Around the room (or in their studios) you have a canvas and paint, a block of clay, a pile of metal, and a box of vinyl. Your job is to assemble a work of art that incorporates all those elements together, blends them where appropriate, and is clear about how the pieces all fit together. You have a limited time to accomplish this. Art critics will be visiting after you’re finished to evaluate your work. Go.

Here are my list of thoughts on how to approach this kind of problem.

  1. Don’t think of this as a collaborative paper. In all likelihood the actual driving of the paper will be done by one person, and that’s you. If you wait around for everyone to chime in, contribute, take ownership for their sections, you will never get anything done. If you aren’t the leader of the paper, but the leader isn’t leading it MAY be possible to just start the process and take leadership. This can be politically dangerous and really depends on the specifics of the project and collaborations, but it’s something to keep in mind. You could be a hero.
  2. Think of this as a collaborative paper. This is a collaborative effort. I realize that this is directly contradictory to my first point. However, it is very important that you don’t lose sight of the fact that you are not the expert in many areas of the paper that you have to put together. Make use of others’ expertise but try to put this in direct requests for input of well-defined portions.
  3. Have a basic understanding of each component. This is really important. Everyone has different expertise and you will not become an expert in a new area by writing a paper. Don’t try. But if there are things that you really are not familiar with that need to go into the paper brush up on them by reading (actually reading from start to finish) previous papers from the group or current review articles in the area. This will allow you to understand at least where the collaborator is coming from and what they can offer.
  4. Don’t overload collaborators with many outlines and drafts. This will only make your collaborators stop paying attention. Instead try to put out one or two outlines, with discussion (teleconference or in person) between. Also with the draft, work with individuals to get portions completed instead of doing everything in multiple rounds of drafts that are commented on by everyone.
  5. Choose a way of collaborating on writing and communicate it with contributors. If you use MS Word for drafts make sure everyone uses the “Tracking Changes” option turned on. Otherwise it’s a nightmare to figure out what parts have been changed. Part of your job will be to manually merge all these changes into a single document. This is a tremendous pain in the ass, but it allows you to evaluate all contributions and make decisions about what to include or how things should be worded. Google Docs seems to work well for producing drafts collaboratively, but at some point the draft should be moved to a single document for finalization.
  6. At the early stages include, don’t exclude. Welcome everyone’s input and suggestions. At some point it may be necessary to make hard decisions about directions of the paper and that may make people unhappy. That’s something you have to live with- but try to listen to the group about these decisions. If there are people with suggestions on more work to do (either experiments/analysis or writing) and their suggestions seem reasonable, make it clear that it’s up to them to carry through with the actual work and try to get a timeline from them for completion. If their piece is essential to the project make sure that you have a plan for extracting this from them- there’s probably a nicer way to put this, but that’s the idea.
  7. At the later stages don’t let newcomers (or others) distract from the plan. If they have really great suggestions, listen to them. If their suggestions seem to distract from the story you are telling fall back on the, “well that’s a great idea, why don’t you investigate that and we can include it if the reviewers request it”- that is, after submission and review.
  8. Have a strategy to create the story you’re going to tell. It can be very difficult to start on a paper cold, when there’s only been discussion about what should be done. A reasonable approach is to do some preliminary analysis yourself then take this to the larger group for input. Make it clear that this is only one possible path and that you’re just trying to promote discussion. Make sure you’re telling a story- this is actually what a scientific paper is about. Be flexible about what the story is. It has to be consistent with the data available- but you may choose to incorporate portions of the results and leave out others that do not help the story along. See also my post on how to write a scientific paper.
  9. Try to avoid redundant effort. Generally this isn’t an issue because everyone is an expert in different areas so the actual work shouldn’t be redundant. Sometimes data analysis needs to be defined to avoid redundancy. If there are large sections to be written (such as an Introduction) it’s better to break it into smaller bits for different people to work on and call this out in the outline or draft so people are clear on who’s doing what. Everyone can revise/comment on all sections toward the end and that’s easier to merge than two disparate documents that are trying to talk about the same thing.
  10. Navigate author order and authorship carefully. This is tremendously important for most people on the project. The critical positions to identify are first author and last author (for biology papers anyway). If you are leading the paper you should be first author, but always remember that for many journals you can specify two or even three ‘first’ authors. For this kind of paper that might be necessary. Don’t try to limit authorship too much. These kinds of papers will have lots of authors. But try to be consistent; if you accept suggestions from everyone’s groups wholesale, it can cause conflicts. Consider that one group might consider technicians who performed the work to be worthy of authorship. If you say OK to this the other groups may chime in with all their technicians, etc. Follow the rules of authorship that you feel comfortable with and believe are ethically consistent, but remember that many, many people may have made significant contributions to the paper. This can be one of the most politically treacherous portions of the paper- have fun!
  11. Find a champion. Identify a senior author who you can communicate with and who you believe will support your positions, or at least will listen to your positions. There may arise situations that require having someone with authority agreeing with you to get others to fall in line.

Finally, here’s an example of a large collaborative research paper that I’ve recently published. It didn’t turn out quite as grand as I’d hoped (what paper does?) but it’s still a nice example of integrating the input of many different groups. I am currently working on (leading) at least three more such papers that are in various stages of being completed.

McDermottJE, ShankaranH, EisfeldAJ, BelisleSE, NeumanG, LiC, McWeeneyS, SabourinC, KawaokaY, Katze MG, Waters KM. (2011). Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems. BMC Systems Biology. 5(1):190. 

Invisible science and how it’s too easy to fool yourself

If you want the full effect of this post, watch the video below first before reading further.

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So, did you see it? I did, probably because I was primed to watch for it, but apparently 50% of subjects don’t!

I heard about a really interesting psychology experiment today (and I LOVE these kinds of things that show us how we’re not as smart as we think we are) called the invisible gorilla experiment. The set up is simple, the subjects watch a video of kids passing balls back and forth. The kids are wearing either red or white shirts. The object is to count the number of times a ball is passed between kids with white shirts. It takes concentration since the kids are moving and mixing and tossing fast. At some point a gorilla walks into view, beats its chest, and walks off. Subjects are then asked if they saw a gorilla. Surprisingly (or not- because it’s one of THESE kinds of experiments) 50% of the subjects don’t remember seeing a gorilla. What they’ve been told to look for and pay attention to is the ball and the color of shirts- gorillas don’t figure in to that equation and your brain, which is very good at filtering out irrelevant information, filters this out.

Anyway, it got me thinking about how we do science. Some of the most interesting, useful, exciting, groundbreaking results in science arise from the unexpected result. You’ve set up your experiment perfectly, you execute it perfectly, and it turns out WRONG! It doesn’t fit with your hypothesis, but in some weird way. Repeat the experiment a few times. If that doesn’t fix the problem then work on changing the experiment until you get rid of that pesky weird result. Ahhhh, there, you’ve ‘fixed’ it- now things will fit with what you expected in the first place.

Most of the time spurious, weird results are probably just that- not very interesting. However, there are probably a lot of times when there are weird results that we as scientists don’t even see. We don’t expect to see them, so we don’t see them. And those could be incredibly interesting. I can see this as being the case in what I do a lot, analysis of high-throughput data (lots of measurements for lots of components at the same time- like microarray expression data). It’s sometimes like trying to count the number of times the kids wearing white shirts pass the ball back and forth- but where there are 300 shirt colors and 2500 kids. Ouch. A gorilla wandering into that mess would be about as obvious as Waldo in a multi-colored referees’ convention. That is, not so much. I wonder how many interesting things are missed and how important that is. In high throughput data analysis often times the goal is to focus on what’s important and ignore the rest- but if the rest is telling an important and dominant story we’re really missing the boat.

I’ve found that one of the best things I can do in my science is to be my own reviewer, my own critic, and my own skeptic. If some result turns out exceptionally well I don’t believe it. Actually there’s an inverse correlation between my belief and the quality of the result with what I expect. I figure if I don’t do this someone down the line will- and it will come back to me. I try to eliminate all the other possibilities of what could be going on (using the scientific method algorithm I’ve previously described). I try to rigorously oppose all my findings until I myself am convinced. However, studies like the invisible gorilla really make me wonder how good I am at seeing things that I’m not specifically looking for.

 

Upside/Downside: Getting a grant rejected

So I’m starting a new feature to talk about the things related to my professional career that I’m ambivalent about. This is for my millions of followers/subscribers who might be interested in this kind of thing.

Receiving a rejection for a grant proposal

Introduction

So part of what I do is writing applications for funding to send off to various funding agencies (e.g. NIH, DOE, etc.). These proposals are reviewed by a panel of my peers- evaluated for quality, innovation, impact, and how well they fit the goals of the request for proposals that I’m answering. A standard NIH R01 grant runs 12 pages and takes several months of preparation and work to assemble and get perfect- it generally involves a lot of personal investment; time, effort, emotional attachment.  In this funding environment (very poor) they have a high probability of being rejected. The reasons vary but the effect is the same. No money, no 3-5 years of guaranteed support, no boost to the ego for having your peers recognize your brilliance, no accolades of any kind.

Your grant has been rejected. You may or may not have the possibility of responding to reviewers’ concerns and resubmitting a revised version of the same grant. It’s the end of the world. Or is it?

Downside:

  • You don’t get the money. That sucks.
    wallpapers_wile-e-coyote_04_1280
  • You won’t have support to pursue that really cool plan that you’ve agonized over for so long.
  • You won’t get the ego boost that comes from success. In fact, the opposite. You have a big kick in the pants from the reviewers and the funding agency telling you that you didn’t make the cut. That sucks too.
  • The reviewers saw major flaws in some part of your proposal, or you didn’t sell it well enough. In either case, you need to take this to heart seriously.

Upside:

  • You’ve spent a good deal of time seriously thinking about this research project. That counts for something. Really. Now you have a plan of action. If you are lucky enough to have some kind of funding to get some part of this done then you now know what 7395256948_e355c85bc3_bto do.
  • These kinds of efforts are very good at highlighting where you need more preliminary data. Maybe you can figure out a way to get some of that accomplished to provide preliminary data for the next round.
  • You now should have a set of good suggestions about where you need to improve and generally, if you read between the lines, you can figure out where your sales pitch has gone wrong and you haven’t made yourself clear.
  • Writing up the background and significance for a grant and gathering all the references necessary is approximately equivalent to writing a review article. Make it one. I’ve done this successfully in several situations.
  • You now have an additional corpus of text, ideas, and figures to recycle for the next grant proposal. And you should do this as much as possible.

Summary

So, in summary. If you’re in the sciences get used to rejection. A lot of rejection. Come to embrace it, accept it, and love it (OK, maybe not love it) and your life will be considerably less stressful. The simple truth is that a lot of people have a lot of great ideas. If the idea is really good it will persist and grow better with time and revisions. Hopefully someday it will land you a big bunch of dough. I know I’m still holding out that hope.