Papers published begets more papers published

So what?


In a recent article first-authored by William Laurance researchers report that, rather unremarkably, publishing more papers before receiving a PhD predicts that an individual will have a more successful career in research, measured solely by publication frequency. They also considered first language, gender precociousness of first article, and university prestige. If publication frequency before attaining the PhD is the best predictor of career publication frequency, just how good is it? They report an r2 value of about 0.14 for the best model incorporating pre-PhD publications, with models lacking this predictor faring much worse.

Wait, what?

If I have a model that only explains 14% of the deviance of the data, well, I think it is time to find a new model. When they included the first three years immediately following attaining a PhD, the r2 value jumped to 0.29 for publications alone, and slightly better when the model includes one or more of the other predictors. Better, but still pretty pathetic. If you are hiring people with a 29% rate of picking the right candidate based on some metric of success chances are you won’t be in charge of hiring for long. The paper only looked at the first ten years immediately following the PhD degree, so including the first three years is a bit like predicting rain when you are already wet. Why were the models so miserable? The range of publication frequency over the first ten years was pretty wide, from 0 to 87 papers published. On top of that, their sample consisted only of individuals who had managed to land a university faculty job. That’s right, one or more of these scientists landed a tenure-track position with zero publications. Jealous?

The sample selection is a pretty major flaw of the paper, in my opinion. The scientists surveyed were all on one rung or another of the assistant/associate/full professor ladder, which is to say that everyone they considered were extremely high acheivers among the total population of people holding biology PhDs. The rate of biology PhDs attaining faculty positions six years post-degree has dropped from 55% in 1973, to 15% in 2006 [1]. Since their data only represented successful academics, their models had no chance of predicting which individuals would drop out of research altogether as opposed to going on to become a principal investigator. Predicting whether an individual is able and willing to continue in science research would be a lot more telling than whether they published 2 versus 10 articles per year their first decade out of grad school.

Using publication frequency as the sole measure of success is certainly rife with limitations (though they do mention a close correlative agreement with h-index). What about quality? What about real, meaningful, contributions to the field? What about retractions? I would be much more interested in a model that could predict whether a researcher would have to withdraw an article during their career than how many articles they might generate. Hopefully with a bit better r2 than 0.14, though.

Publication is often referred to as the “currency” of academia. Well I’d like to posit that this currency is purely fiat. If inflation continues as it has been doing [2], the rate of fraudulent papers can only increase [3]. In my estimation, 300 papers with 3 retractions is worth a lot less than a “measly” 30 papers total. The commonplace occurrence of papers that must be withdrawn (not to mention fraudulent papers never outed, frivolous claims and tenuous conclusions) has broader implications beyond an individual’s career or a journal’s bottom line. When bad science becomes the new normal, public trust deteriorates, and anti-science sentiments thrive.

The authors of the paper did have what I would consider a good take-home: faced with two applicants, one with a PhD from a prestigious university and the other from a lesser-known institution, pick the one with the better publication record. I would go one further and encourage hiring decisions to be informed by actually reading the papers. And vet the sources in these papers’ references. It’s not too hard, and if your job description includes hiring new talent, it’s your job. ‘A’s hire ‘A’s, and ‘B’s hire ‘C’s. Don’t be a ‘B,’ Science (with a capital ‘S’) depends on it.

Laurance et al Predicting Publication Success for Biologists Bioscience Oct. 2013

via conservation bytes

DEAR ABBE: What’s with the “twinkle” in this Hubble image?


the Spirit of Ernst Abbe
Legendary physicist Ernst Abbe answers your photonics questions

DEAR ABBE: I was cruising around the internet the other day in my web-rocket when I came across this stellar image of the comet ISON, taken by the Hubble space telescope. The stars appear to be twinkling. I was under the impression that the twinkling effect we see on earth is due to the atmosphere, and last time I checked the Hubble was something of a space telescope, so shouldn’t Hubble be above twinkling? -HUMBLED BY HUBBLE

DEAR HUMBLED: You’re right about twinkling, it is not apparent to observers located outside of a dense atmosphere, the topic of the 1969 paper “Importance of observation that stars don’t twinkle outside the earth’s atmosphere” by astronaut Walt Cunningham and co-author L. Marshall Libby. But twinkling is not likely to produce such picturesque points on stars as you see in that Hubble image. Rather, what appears to the naked eye as twinkling will serve to blur and smudge the image of a star in a time-averaged intensity measurement, such as a photograph.


The spikes you see in the image in question are due to something else entirely. Twinkling stars are a result of a fickle refractive media, the atmosphere, inadvertently being included in an imaging system. The culprits causing these spikes are intentionally built into the optical system, though the effect on the image formed is a byproduct of their form rather than their primary function. What you see as four regular points oriented to the same direction on every bright star is actually the result of diffraction around the secondary mirror support struts[2][3]. Since the spikes are the Fourier transform of the struts themselves[4], they will affect every light source in the image according to their shape and brightness. The appearance of diffraction spikes is so common that the human mind essentially expects it in this type of image, and can be considered aesthetic. Ultimately, though, any light ending up in the diffraction spikes is light that could have contributed to forming the accurate image of the scene. If a dim object of interest resides by a very bright point of light, the diffraction spikes of the latter can interfere with the clear few of the dim object.

Hubble’s successor, the James Webb telescope will have three struts rather than four[5], resulting in a very different set of diffraction spikes. Not only will the James Webb struts differ in number, but these will be arranged in a sort of triangular pyramid. Diffraction around the strut will affect the final image differently at different lengths along each strut, because they will occupy a range of distances from the primary mirror. The resulting spikes should be quite interesting.

Comet ISON image available at

Do you have a question for Abbe? Ask it in the comments or tweet it @theScinder

Referencing “I Fucking Love Science” in Your Article Title is a Great Way to Attract Web Traffic

Negging your audience is also important, apparently.


John Skylar had an interesting article last week, picked up by Mashables shortly after it went a bit viral. He points out that the beautiful images posted on I Fucking Love Science (hereafter referred to as IFLS) are not all that great of a representation of what science is, and fawning over them does not equate a “love of science,” (though I would argue that stock photography of people pipetting is also “not science”). After I point out that Skylar’s seemingly hostile position on IFLS was actually just an aggressive technique for internet-writers to drive traffic, I want to quickly defend IFLS for what it actually provides. IFLS is what science students, profs and professionals would refer to as their outreach work: it drives interest and awareness among the general populace, many who would never be exposed to it otherwise. For those working in scientific fields, it provides a window into other fields as well. Usually the image is accompanied by a short explanatory text and a link to the original article, or at least the popular press take on it. I would expect that the nominal goal for outreach, not just IFLS, is to increase scientific literacy in the general population. But once again there is a pervasive conflation of terms, and it isn’t confusing data with people and money, it is conflating results with a method. I would claim that in actuality, Mythbusters is potentially better for improving scientific literacy than IFLS, and zombie Richard Feynman would back me up.

I studied science and engineering at the undergraduate and graduate level for about five years, doing a little research along the way to boot. In all of my coursework, I can’t recall taking a single class that actually taught science. The coursework that probably came the closest was in statistics. In lieu of teaching a few courses a year on the intricacies of the scientific method, elegant experiments, etc., courses in a science department almost invariably teach the history and current consensus of a field, by and large treating this information as static facts. In short, they focus on the results and tend to ignore where these results come from. The contents of the science coursework taught at a typical university is not science, it is trivia.

Science is a method for figuring out if an idea we have about the world is false, nothing more. You have heard this before if you ever competed in the science fair as a kid. Science is a comparison of guesses and givens, where guesses are the results we expect if some idea we have about the world might be true. Givens are the data, gathered by observations, measurement, and sometimes assumption. Where the guesses and the givens don’t agree, the original idea is WRONG, simple as that. The best science is based on the ideas that are most readily falsifiable, not necessarily the most complicated. A vague theory is hard to disprove, but it still can be very, very useless.

Ultimately, scientific literacy is not knowing how many flarks jive a phoouon with a 90 rad spin, but the ability to be confronted with a claim and confer upon it a vote of confidence or no confidence in that idea’s reality. The best way to love science is to use it to inform your view of the world, regardless of your profession. Next time another human tries to sell you on an idea, ask to see their p-value, and don’t trust averages without error bars. If you start with an unsparing application of science, the survival rates of nonsense will plummet. Maybe that effect would even trickle up to the higher echelons of U.S. government, and eventually they might enact reasonable policies with an outlook beyond four years at a time, including emphasising a strong and stable investment in academic research.

Hat tip to Neal Stephenson’s novel Anathem for influencing my interpretation of “givens.”

Feynman has the best description of science that I have yet found.

Image from originally from the book “Mendel’s Principles of Heredity: A Defence.” Scans of book at

Stop Saying Dynamical


Following close behind experimental testing of falsifiable hypotheses, the secondary responsibility of a scientist is arguably clear communication of results. Given that the majority of research is ultimately funded by the tax-paying public, it is important that outcomes are eventually conveyed in a manner that can be understood by an intelligent layperson. Increased scientific literacy in policy makers and their constituents is a prerequisite to face modern challenges such as changing climate, public health, and the consequences of population pressure. Effective outreach to the public is more important than ever. Accepting the previous statement, why is there a continuing trend among scientists to mask communicative content through cryptic language, particularly when perfectly acceptable and widely recognized terms are available? I’ll focus on what I consider to be the most obvious and ridiculous offender, the great scourge of scientific writing, faculty information pages, and grant proposals; the great occluder of meaning, intimidator of readers, the entirely redundant bit of lexicon: dynamical.

Dynamical, like its more accessible and less attention-hungry sibling word dynamic, has its roots in the Greek dynamikos, meaning powerful. In general both terms relate to something that changes with time. Since both “dynamical” and “dynamic” function as adjectives, they are essentially interchangeable, the only difference between that I have ever been able to discern is the demographics of their use. “Dynamical” is used by physicists, mathematicians and engineers who work in dynamical systems theory, a branch of mathematics dealing with systems described by differential (if continuous) or difference (if discrete) equations. The additional suffix “-al” that delineates the two words seems to have been born of single, somewhat malicious intent: to serve as brick and mortar in the construction of an ivory tower separating scientists and small-folk. It is exactly this sort of word choice that leads to the perception that scientists have more smarts than sense and that they produce results that ultimately fail to have any application to the real world. Ultimately this serves as fuel for the anti-science fire burning through the minds of policy makers and the public. Consider the following two sentences and the impression they would leave on a reader over a morning coffee:

“We utilize the time-slice method as a means of dynamical downscaling to specify expected climate change for Southern Europe”

“We utilize the time-slice method as a means of dynamic downscaling to specify expected climate change for Southern Europe”


U. Cubaschll, H. von Storch, J. waszkewitz, E. zorita. Estimates of climate change in Southern Europe derived from dynamical climate model output . Climate Research . November 29 , 1996.

Even though the sentence makes reference to specific methods that a non-specialist reader might not be familiar with, the language is descriptive enough to impart a conceptual understanding of what the authors describe, except for that cumbersome “dynamical,” which throws the whole thing into question. It reads as if it came from a humour piece poking fun at absent-minded professor types. The null-meaning suffix implies there is meaning above and beyond the root word where there is none, it just sounds more complicated. This is not an outcome that scientists should strive for, no matter how intelligent it makes them feel to use it.

As disciplines in life science become increasingly concerned with complexity and modeling, I expect the number of life scientists interested in studying dynamic systems will only continue to rise. Given the particularly relevant nature of life sciences to understanding our relationship to our living planet, I beg you, wherever possible, to avoid using the word dynamical. The physicists, mathematicians, and engineers may be entrenched in their devotion to the nonsense word, but there’s no reason for this senseless departure from clarity to infect biologists, ecologists, biochemists, etc. any more than it already has. The arbitrary and counterintuitive way that scientists name the genes they discover-a combination of sarcasm, mystery and the opposite of their function-is a big enough mess.


Consider this an invitation to attempt to delineate the dynamic/dynamical word pair in the comments.