Making a super villain

I’ve written about Reviewer 3 before (here, here, here, and here). Somehow the third reviewer has come to embody the capriciousness (and sometimes meanness) of the anonymous peer review process. Note that I believe in the peer review process, but am a realist about what it means and what it accomplishes. It doesn’t mean that every paper passing peer review is perfect and it doesn’t mean that every peer reviewer is doing a great job of reviewing.

When I’m a reviewer I see the peer review process through the lens of the line from Spiderman (Stan Lee), “with great power comes great responsibility”. I strive to put as much effort in to each paper I review as I would expect and want from the reviewers who review my papers. Sometimes that means that I don’t get my reviews back exactly on time- but better that than a crappy, half-thought-through review. I’m not sure that I always succeed. Sometimes I think that I may have missed points made by the authors, or I may have the wrong idea about an approach or result. However, if I’ve done a good job of trying to get it right the peer review process is working.


More Science Caution Signs

You asked for it (you don’t remember? Well, you did) so you got it. More science caution signs.

This time I had some help. See the contributions of ideas from:

And there were some other ideas too that I just haven’t put into a visual representation yet- so there may be another installment of these important warning signs in the future.



Academic Halloween Costumes

It’s that time of year. You’ve been invited to a costume party and now must decide: what will you dress up as? Well, I’ve assembled some of the BEST Halloween costume ideas for overworked, stressed out, over thinking academics right here. If you have further suggestions please Tweet them with the hashtag #academiccostumes.



I’ve posted before about some of my organizational approaches (and attempts at it) but it can sometimes be impossible not to get overwhelmed and busy. Being busy on multiple tasks, with multiple deadlines can be a killer, but sometimes it crystallizes a resolve to move some of those items off your todo list and you increase your overall effectiveness (you know, less Twitter and blogging and comic-making and stuff). I’ve also seen people who seem to make it a part of their academic persona to be perpetually too busy. This seems to be considered a status symbol (often times mostly by the person being so busy). The key to busy-ness and keeping your head above water (and the seals at bay) is balance. Make sure to keep perspective about what you’re doing and know that often (maybe always) banging your head against the same task for hours on end is counterproductive.

Anyway, here’s a handy tool to help you assess your level of busy-ness, fresh from the RedPen/BlackPen labs.


The Truth

What do you think the truth is? That is what do you think the concept of “truth” actually means? Is it an absolute- a destination that you can reach if you just try hard enough? Or is it something else? A road that stretches out in front of you and constantly changes as you progress and add more evidence?



Phenomenal science powers!

So you toil for 4+ years in graduate school, 4+ years as a post-doc, land your first academic gig. Now you get to do all this awesome science, right? Well, sorta…

Oy! 10,000 years in the cave of graduate school will give you SUCH a crook in the neck!

Oy! 10,000 years in the cave of graduate school will give you SUCH a crook in the neck!

What is a hypothesis?

So I got this comment from a reviewer on one of my grants:

The use of the term “hypothesis” throughout this application is confusing. In research, hypotheses pertain to phenomena that can be empirically observed. Observation can then validate or refute a hypothesis. The hypotheses in this application pertain to models not to actual phenomena. Of course the PI may hypothesize that his models will work, but that is not hypothesis-driven research.

There are a lot of things I can say about this statement, which really rankles. As a thought experiment replace all occurrences of the word “model” with “Western blot” in the above comment. Does the comment still hold?

At this point it may be informative to get some definitions, keeping in mind that the _working_ definitions in science can have somewhat different connotations.

From Google:

Hypothesis: a supposition or proposed explanation made on the basis of limited evidence as a starting point for further investigation.

This definition has nothing about empirical observation- and I would argue that this definition would be fairly widely accepted in biological sciences research, though the underpinnings of the reviewer’s comment- empirically observed phenomena- probably are in the minds of many biologists.

So then, also from Google:

Empirical: based on, concerned with, or verifiable by observation or experience rather than theory or pure logic.

Here’s where the real meat of the discussion is. Empirical evidence is based on observation or experience as opposed to being based on theory or pure logic. It’s important to understand that the “models” being referred to in my grant are machine learning statistical models that have been derived from sequence data (that is, observation).

I would argue that including some theory or logic in a model that’s based on observation is exactly what science is about- this is what the basis of a hypothesis IS. All the hypotheses considered in my proposal were based on empirical observation, filtered through some form of logic/theory (if X is true then it’s reasonable to conclude Y), and would be tested by returning to empirical observations (either of protein sequences or experimentation at the actual lab bench).

I believe that the reviewer was confused by the use of statistics, which is a largely empirical endeavor (based on the observation of data- though filtered through theory) and computation, which they do not see as empirical. Back to my original thought experiment, there’s a lot of assumptions, theory, and logic that goes into interpretation of Western blot – or any other common lab experiment. However, this does not mean that we can’t use them to formulate further hypotheses.

This debate is really fundamental to my scientific identity. I am a biologist who uses computers (algorithms, visualization, statistics, machine learning and more) to do biology. If the reviewer is correct, then I’m pretty much out of a job I guess. Or I have to settle back on “data analyst” as a job title (which is certainly a good part of my job, but not the core of it).

So I’d appreciate feedback and discussion on this. I’m interested to hear what other people think about this point.

Proposal gambit – Betting the ranch

Last spring I posted about a proposal I’d put in where I’d published the key piece of preliminary data in F1000 Research, a journal that offers post-publication peer review.

The idea was that I could get my paper published (it’s available here) and accessible to reviewers prior to submission of my grant. It could then be peer-reviewed and I could address the revisions after that. This strategy was driven by the lag time between proposal submission and review for NIH, which is about 4 months. Also, it used to be possible to include papers that hadn’t been formally accepted by a journal as an appendix to NIH grants. This hasn’t been possible for some time now. But I figured this might be a pretty good way to get preliminary data out to the grant reviewers in a published form with quick turnaround. Or at least that you could utilize that lag time to also function as review time for your paper.

I was able to get my paper submitted to F100 Research and obtained a DOI and URL that I could include as a citation in my grant. Details here.

The review for the grant was completed in early June of this year and the results were not what I had hoped- the grant wasn’t even scored, despite being totally awesome (of course, right?). But for this post I’ll focus on the parts that are pertinent to the “gambit”- the use of post-publication peer review as preliminary data.

The results here were mostly unencouraging RE post-publication peer review being used this way, which was disappointing. But let me briefly describe the timeline, which is important to understand a large caveat about the results.

I received first-round reviews from two reviewers in a blindingly fast 10 and 16 days after initial submission. Both were encouraging, but had some substantial (and substantially helpful) requests. You can read them here and here. It took me longer than it could have to address these completely – though I did some new analysis and added additional explanation to several important points. I then resubmitted on around May 12th or so. However, due to some kind of issue the revised version wasn’t made available by F1000 Research until May 29th. Given that the NIH review panel met in the first week of June it is likely that the reviewers didn’t see the revised (and much improved version). The reviewers then got back final comments in early June (again- blindingly fast). You can read those here and here. The paper was accepted/approved/indexed in mid-June.

The grant had comments from three reviewers and each had something to say about the paper as preliminary data.

The first reviewer had the most negative comments.

It is not appropriate to point reviewers to a paper in order to save space in the proposal.

Alone this comment is pretty odd and makes me think that the reviewer was annoyed by the approach. So I can’t refer to a paper as preliminary data? On the face of it this is absolutely ridiculous. Science, and the accumulation of scientific knowledge just doesn’t work in a way that allows you to include all your preliminary data completely (as well as your research approach and everything else) in the space of 12 page grant. However, their further comments (which directly follow this one) shed some light on their thinking.

The PILGram approach should have been described in sufficient detail in the proposal to allow us to adequately assess it. The space currently used to lecture us on generative models could have been better used to actually provide details about the methods being developed.

So reading between the (somewhat grumpy) lines I think they mean to say that I should have done a better job of presenting some important details in the text itself. But my guess is that the first reviewer was not thrilled by the prospect of using a post-publication peer reviewed paper as preliminary data for the grant. Not thrilled.

  • Reviewer 1: Thumbs down.

Second reviewer’s comment.

The investigators revised the proposal according to prior reviews and included further details about the method in the form of a recently ‘published’ paper (the quotes are due to the fact that the paper was submitted to a journal that accepts and posts submissions even after peer review – F1000 Research). The public reviewers’ comments on the paper itself raise several concerns with the method proposed and whether it actually works sufficiently well.

This comment, unfortunately, is likely due to the timeline I presented above. I think they saw the first version of the paper, read the paper comments, and figured that there were holes in the whole approach. If my revisions had been available it seems like there still would have been issues, unless I had already gotten the final approval for the paper.

  • Reviewer 2: Thumbs down- although maybe not with the annoyed thrusting motions that the first reviewer was presumably making.

Finally, the third reviewer (contrary to scientific lore) was the most gentle.

A recent publication is suggested by the PI as a source of details, but there aren‟t many in that manuscript either.

I’m a little puzzled about this since the paper is pretty comprehensive. But maybe this is an effect of reading the first version, not the final version. So I would call this neutral on the approach.

  • Reviewer 3: No decision.


The takeaway from this gambit is mixed.

I think if it had been executed better (by me) I could have gotten the final approval through by the time the grant reviewers were looking at it and then a lot of the hesitation and negative feelings would have gone away. Of course, this would be dependent on having paper reviewers that were as quick as those that I got- which certainly isn’t a sure thing.

I think that the views of biologists on preprints, post-publication review, and other ‘alternative’ publishing options are changing. Hopefully more biologist will start using these methods- because, frankly, in a lot of cases they make a lot more sense than the traditional closed-access, non-transparent peer review processes.

However, the field can be slow to change. I will probably try this, or something like this, again. Honestly, what do I have to lose exactly? Overall, this was a positive experience and one where I believe I was able to make a contribution to science. I just hope my next grant is a better substrate for this kind of experiment.

Other posts on this process: