Author Archives: Tal Yarkoni

Apparently, many scientists have rather strong feelings about data sharing mandates. In the wake of PLOS’s recent announcement–which says that, effective now, all papers published in PLOS journals must deposit their data in a publicly accessible location–a veritable gaggle of scientists have taken to their blogs to voice their outrage and/or support for the policy. The nays have posts like DrugMonkey’s complaint that the inmates are running the asylum at PLOS (more choice posts are here, here, here, and here); the yays have Edmund Hart telling the nays to get over themselves and share their data (more posts here, here, and here). While I’m a bit late to the party (mostly because I’ve been traveling and otherwise indisposed), I guess I’ll go ahead and throw my hat into the ring in support of data sharing mandates. For a number of reasons outlined below, I think time will show the anti-PLOS folks to very clearly be on the wrong side of this issue.

Mandatory public deposition is like, totally way better than a “share-upon-request” approach

You might think that proactive data deposition has little incremental utility over a philosophy of sharing one’s data upon request, since emails are these wordy little things that only take a few minutes of a data-seeker’s time to write. But it’s not just the time and effort that matter. It’s also the psychology and technology. Psychology, because if you don’t know the person on the other end, or if the data is potentially useful but not essential to you, or if you’re the agreeable sort who doesn’t like to bother other people, it’s very easy to just say, “nah, I’ll just go do something else”. Scientists are busy people. If a dataset is a click away, many people will be happy to download that dataset and play with it who wouldn’t feel comfortable emailing the author to ask for it. Technology, because data that isn’t publicly available is data that isn’t publicly indexed. It’s all well and good to say that if someone really wants a dataset, they can email you to ask for it, but if someone doesn’t know about your dataset in the first place–because it isn’t in the first three pages of Google results–they’re going to have a hard time asking.

By now you will most likely have heard about the “Many Labs” Replication Project (MLRP)–a 36-site, 12-country, 6,344-subject effort to try to replicate a variety of classical and not-so-classical findings in psychology. You probably already know that the authors tested a variety of different effects–some recent, some not so recent (the oldest one dates back to 1941!); some well-replicated, others not so much–and reported successful replications of 10 out of 13 effects (though with widely varying effect sizes).

By and large, the reception of the MLRP paper has been overwhelmingly positive. Setting aside for the moment what the findings actually mean (see also Rolf Zwaan’s earlier take), my sense is that most psychologists are united in agreement that the mere fact that researchers at 36 different sites were able to get together and run a common protocol testing 13 different effects is a pretty big deal, and bodes well for the field in light of recent concerns about iffy results and questionable research practices.

But not everyone’s convinced. There now seems to be something of an incipient backlash against replication. Or perhaps not so much against replication itself as against the notion that the ongoing replication efforts have any special significance. An in press paper by Joseph Cesario makes a case for deferring independent efforts to replicate an effect until the original effect is theoretically well understood (a suggestion I disagree with quite strongly, and plan to follow up on in a separate post). And a number of people have questioned, in blog comments and tweets, what the big deal is. A case in point:

10/13 cherry-picked studies replicate which means precisely nothing. Replication hysteria makes us dumber by the day. Continue reading →

I took the plunge a couple of days ago and answered my first question on Quora. Since Brad Voytek won’t shut up about how great Quora is, I figured I should give it a whirl. So far, Brad is not wrong.

The question in question is: “How much do you agree with Johnathan Katz’s advice on (not) choosing science as a career? Or how realistic is it today (the article was written in 1999)?” The Katz piece referred to is here. The gist of it should be familiar to many academics; the argument boils down to the observation that relatively few people who start graduate programs in science actually end up with permanent research positions, and even then, the need to obtain funding often crowds out the time one has to do actual science. Katz’s advice is basically: don’t pursue a career in science. It’s not an optimistic piece.

My answer is, I think, somewhat more optimistic. Here’s the full text:

The real question is what you think it means to be a scientist. Science differs from many other professions in that the typical process of training as a scientist–i. Continue reading →

Over the past two years, my scientific computing toolbox been steadily homogenizing. Around 2010 or 2011, my toolbox looked something like this:

• Ruby for text processing and miscellaneous scripting;
• Ruby on Rails/JavaScript for web development;
• Python/Numpy (mostly) and MATLAB (occasionally) for numerical computing;
• MATLAB for neuroimaging data analysis;
• R for statistical analysis;
• R for plotting and visualization;
• Occasional excursions into other languages/environments for other stuff.

In 2013, my toolbox looks like this:

• Python for text processing and miscellaneous scripting;
• Ruby on Rails/JavaScript for web development, except for an occasional date with Django or Flask (Python frameworks);
• Python (NumPy/SciPy) for numerical computing;
• Python (Neurosynth, NiPy etc. Continue reading →

The title pretty much says it. After spending four great years in Colorado, I’m happy to say that I’ll be moving to Austin at the end of the month. I’ll be joining the Department of Psychology at UT-Austin as a Research Associate, where I plan to continue dabbling in all things psychological and informatic, but with less snow and more air conditioning.

While my new position nominally has the same title as my old one, the new one’s a bit unusual in that the funding is coming from two quite different sources. Half of it comes from my existing NIH grant for development of the Neurosynth framework, which means that half of my time will be spent more or less the same way I’m spending it now–namely, on building tools to improve and automate the large-scale synthesis of functional MRI data. (Incidentally, I’ll be hiring a software developer and/or postdoc in the very near future, so drop me a line if you think you might be interested.)

The other half of the funding is tied to the PsyHorns course developed by Jamie Pennebaker and Sam Gosling over the past few years. PsyHorns is a synchronous massive online course (SMOC) that lets anyone in the world with an internet connection (okay, and $550 in loose change lying around) take an introductory psychology class via the internet and officially receive credit for it from the University of Texas (this recent WSJ article on PsyHorns provides some more details). My role will be to serve as a bridge between the psychologists and the developers–which means I’ll have an eclectic assortment of duties like writing algorithms to detect cheating, developing tools to predict how well people are doing in the class, mining the gigantic reams of data we’re acquiring, developing ideas for new course features, and, of course, publishing papers.

Naturally, the PILab will be joining me in my southern adventure. Since the PILab currently only has one permanent member (guess who?), and otherwise consists of a single Mac Pro workstation, this latter move involves much less effort than you might think (though it does mean I’ll have to change the lab website’s URL, logo, and–horror of horrors–color scheme). Continue reading →

I’m working on a TOP SEKKRIT* project involving large-scale data mining of the psychology literature. I don’t have anything to say about the TOP SEKKRIT* project just yet, but I will say that in the process of extracting certain information I needed in order to do certain things I won’t talk about, I ended up with certain kinds of data that are useful for certain other tangential analyses. Just for fun, I threw some co-authorship data from 2,000+ Psychological Science articles into the d3.js blender, and out popped an interactive network graph of all researchers who have published at least 2 papers in Psych Science in the last 10 years**. It looks like this:

You can click on the image to take a closer (and interactive) look.

I don’t think this is very useful for anything right now, but if nothing else, it’s fun to drag Adam Galinsky around the screen and watch half of the field come along for the ride. There are plenty of other more interesting things one could do with this, though, and it’s also quite easy to generate the same graph for other journals, so I expect to have more to say about this later on.

* It’s not really TOP SEKKRIT at all–it just sounds more exciting that way.

** Or, more accurately, researchers who have co-authored at least 2 Psych Science papers with other researchers who meet the same criterion. Continue reading →

You could be forgiven for thinking that academic psychologists have all suddenly turned into professional whistleblowers. Everywhere you look, interesting new papers are cropping up purporting to describe this or that common-yet-shady methodological practice, and telling us what we can collectively do to solve the problem and improve the quality of the published literature. In just the last year or so, Uri Simonsohn introduced new techniques for detecting fraud, and used those tools to identify at least 3 cases of high-profile, unabashed data forgery. Simmons and colleagues reported simulations demonstrating that standard exploitation of research degrees of freedom in analysis can produce extremely high rates of false positive findings. Pashler and colleagues developed a “Psych file drawer” repository for tracking replication attempts. Several researchers raised trenchant questions about the veracity and/or magnitude of many high-profile psychological findings such as John Bargh’s famous social priming effects. Wicherts and colleagues showed that authors of psychology articles who are less willing to share their data upon request are more likely to make basic statistical errors in their papers. And so on and so forth. The flood shows no signs of abating; just last week, the APS journal Perspectives in Psychological Science announced that it’s introducing a new “Registered Replication Report” section that will commit to publishing pre-registered high-quality replication attempts, irrespective of their outcome.

Personally, I think these are all very welcome developments for psychological science. They’re solid indications that we psychologists are going to be able to police ourselves successfully in the face of some pretty serious problems, and they bode well for the long-term health of our discipline. My sense is that the majority of other researchers–perhaps the vast majority–share this sentiment. Still, as with any zeitgeist shift, there are always naysayers. In discussing these various developments and initiatives with other people, I’ve found myself arguing, with somewhat surprising frequency, with people who for various reasons think it’s not such a good thing that Uri Simonsohn is trying to catch fraudsters, or that social priming findings are being questioned, or that the consequences of flexible analyses are being exposed. Continue reading →

Warning: what follows is a somewhat technical discussion of my love-hate relationship with the R statistical language, in which I somehow manage to waste 2,400 words talking about a single line of code. Reader discretion is advised.

I’ve been using R to do most of my statistical analysis for about 7 or 8 years now–ever since I was a newbie grad student and one of the senior grad students in my lab introduced me to it. Despite having spent hundreds (thousands?) of hours in R, I have to confess that I’ve never set aside much time to really learn it very well; what basic competence I’ve developed has been acquired almost entirely by reading the inline help and consulting the Oracle of Bacon Google when I run into problems. I’m not very good at setting aside time for reading articles or books or working my way through other people’s code (probably the best way to learn), so the net result is that I don’t know R nearly as well as I should.

That said, if I’ve learned one thing about R, it’s that R is all about flexibility: almost any task can be accomplished in a dozen different ways. I don’t mean that in the trivial sense that pretty much any substantive programming problem can be solved in any number of ways in just about any language; I mean that for even very simple and well-defined tasks involving just one or two lines of code there are often many different approaches.

To illustrate, consider the simple task of selecting a column from a data frame (data frames in R are basically just fancy tables). Suppose you have a dataset that looks like this:

In most languages, there would be one standard way of pulling columns out of this table. Continue reading →

For the last few months, I’ve been getting a steady stream of emails in my inbox that go something like this:

Dear Dr. Yarkoni,

We recently concluded our search for the position of Assistant Grand Poobah of Academic Sciences in the Area of Multidisciplinary Widget Theory. We received over seventy-five thousand applications, most of them from truly exceptional candidates whose expertise and experience would have been welcomed with open arms at any institution of higher learning–or, for that matter, by the governing board of a small planet. After a very careful search process (which most assuredly did not involve a round or two on the golf course every afternoon, and most certainly did not culminate in a wild injection of an arm into a hat filled with balled-up names) we regret to inform you that we are unable to offer you this position. This should not be taken to imply that your intellectual ability or accomplishments are in any way inferior to those of the person who we ultimately did offer the position to (or rather, persons–you see, we actually offered the job to six people before someone accepted it); what we were attempting to optimize, we hope you understand, was not the quality of the candidate we hired, but a mythical thing called ‘fit’ between yourself and ourselves. Or, to put it another way, it’s not you, it’s us.

We wish you all the best in your future endeavors, and rest assured that if we have another opening in future, we will celebrate your reapplication by once again balling your name up and tossing it into a hat along with seventy-five thousand others.

These letters are typically so warm and fuzzy that it’s hard to feel bad about them. I mean, yes, they’re basically telling me I failed at something, but then, how often does anyone ever actually tell me I’m an impressive, accomplished, human being? Never! If every failure in my life was accompanied by this kind of note, I’d be much more willing to try new things. Continue reading →

This week’s issue of Science has an interesting article on The Big Pitch–a pilot NSF initiative to determine whether anonymizing proposals and dramatically cutting down their length (from 15 pages to 2) has a substantial impact on the results of the review process. The answer appears to be an unequivocal yes. From the article:

What happens is a lot, according to the first two rounds of the Big Pitch. NSF’s grant reviewers who evaluated short, anonymized proposals picked a largely different set of projects to fund compared with those chosen by reviewers presented with standard, full-length versions of the same proposals.

Not surprisingly, the researchers who did well under the abbreviated format are pretty pleased:

Shirley Taylor, an awardee during the evolution round of the Big Pitch, says a comparison of the reviews she got on the two versions of her proposal convinced her that anonymity had worked in her favor. An associate professor of microbiology at Virginia Commonwealth University in Richmond, Taylor had failed twice to win funding from the National Institutes of Health to study the role of an enzyme in modifying mitochondrial DNA.

Both times, she says, reviewers questioned the validity of her preliminary results because she had few publications to her credit. Some reviews of her full proposal to NSF expressed the same concern. Without a biographical sketch, Taylor says, reviewers of the anonymous proposal could “focus on the novelty of the science, and this is what allowed my proposal to be funded.”

Broadly speaking, there are two ways to interpret the divergent results of the standard and abbreviated review. Continue reading →