We have a new paper out! You can read it here. I thought I would write a post on how this paper came to be and also about our first proper experience with preprinting. Title of the paper: Non-specificity of Pitstop 2 in clathrin-mediated endocytosis. In a nutshell: we show that Pitstop 2, a supposedly selective clathrin inhibitor acts in a non-specific way to inhibit endocytosis.

A recent opinion piece published in eLife bemoaned the way that citations are used to judge academics because we are not even certain of the veracity of this information. The main complaint was that Google Scholar – a service that aggregates citations to articles using a computer program – may be less-than-reliable. There are three main sources of citation statistics: Scopus, Web of Knowledge/Science and Google Scholar;

How long does it take to publish a paper? The answer is – in our experience, at least – about 9 months. That’s right, it takes about the same amount of time to have a baby as it does to publish a scientific paper. Discussing how we can make the publication process quicker is for another day. Right now, let’s get into the numbers.

Fans of data visualisation will know the work of Edward Tufte well. His book “The Visual Display of Quantitative Information” is a classic which covers the history and the principals of conveying data in a concise way, that is easy to interpret. He is also credited with two different dataviz techniques: sparklines and image quilts.

How long does it take to publish a paper? I posted the picture below on Twitter to show how long it takes for us to publish a paper.

Das 2014er Gutachten der Expertenkommission Forschung und Innovation (EFI) schlug diese Woche einige Wellen, weil es nach wie vor eine Abwanderung der besten Wissenschaftler aus Deutschland konstatiert ( brain drain , wenn man den Begriff mag). Nach einem Blick in das Gutachten (Abschnitt B2, ab S. 85) sind dazu einige Anmerkungen angebracht, die die Schlagzeilen in den Kontext rücken. Erstens, die Ergebnisse sind wenig überraschend.

I wrote recently that if you want to be confident in generalizing observations from a sample to the entire population, your sample needs to be representative. But maybe you’re skeptical. You might have noticed that a lot of people don’t pay much attention to representativeness, and somehow there are hardly any consequences for them. But that doesn’t mean that there are never consequences, for them or other people.

Martorell, C. & R.P. Freckleton. 2014. Testing the roles of competition, facilitation and stochasticity on community structure in a species-rich assemblage. Journal of Ecology doi:10.1111/1365-2745.12173 At a given location in nature, why are some species present and others absent? Why do some species thrive and have lots of individuals and others are barely eeking out an existence? What determines how many species can live together there?

Recently I’ve talked about the different standards for existential and universal claims, how we can use representative samples to estimate universal claims, and how we know if our representative sample is big enough to be “statistically significant.” But I want to add a word of caution to these tests: you can’t get statistical significance without a representative sample.

Engaging in Art and Science Collaborations This is a guest post by Zack Brym (@ZackBrym). He is a graduate student in our group interested in the form and function of orchard trees. He has also developed an interest in scientific communication.