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. The answer is 235 days. This is the median time from submission at the first journal to publication online or in print. The data are from our last ten papers. The infographic proved popular with 40 retweets and 22 favourites. It was pointed out to me that the a few things would improve this visualisation: 1.

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.

In my post last week I talked about the importance of representative samples for making universal statements, including averages and percentages. But how big should your sample be? You don’t need to look at everything, but you probably need to look at more than one thing. How big a sample do you need in order to be reasonably sure of your estimates?

In my previous post, I discussed the differences between existential and universal statements. In particular, the standard of evidence is different: to be sure that an existential statement is correct we only need to see one example, but to be sure a universal is correct we have to have examined everything. But what if we don’t have the time to examine everything, and we don’t have to be absolutely sure?

Open access and gated publishing have been discussed at length by researchers, publishers, and the media. Still, the debate got more intense when Elsevier sent takedown notices to Academia.edu. Yesterday, it got even more intense when it became public that the University of Calgary is also confronted with takedown notices from Elsevier. Elsevier responded to the critiques, but it is unlikely that this will take the heat out of the debate.