Luckily, there are many roads to open access to publicly funded research.

Editors: Mark Wass (University of Kent, UK), Iddo Friedberg (Miami University, Oxford, Ohio, USA), Predrag Radivojac (Indiana University, Bloomington, Indiana, USA) Last July, GigaScience and the organisers of the Automated Function Prediction special interest group at ISMB announced an upcoming series on Automated Function Prediction.

As the GigaScience journal moves from strength-to-strength, with that comes the expansion of the editorial and data management teams that are now spanning three continents – and what better way to meet than at the 8th International Conference on Genomics (ICG8) in Shenzhen, China, co-organised by the BGI and GigaScience. Held at the Thunderbirdsesque Vanke International Conference Centre in the popular seaside resort of

Doing science in academia involves a lot of rejection and negative feedback. Between grant agencies single digit funding rates, pressure to publish in a few “top” journals all of which have rejection rates of 90% or higher [1], and the growing gulf between the number of academic jobs and the number of graduate students and postdocs [2], spending even a small amount of time in academia pretty much guarantees that you’ll see a lot of rejection.

As big proponents of Open Data, on top of the many diverse datasets associated with GigaScience papers in our integrated GigaDB database, we are continuing to fill it with datasets produced by our BGI hosts.

The rate of species extinction has lent increasing urgency to the description of new species, but in this supposedly networked “big data” era the process of cataloging the rich tapestry of life has changed little since the time of Linnaeus.

Sam Scheiner published a piece recently on ecology’s lack of engagement with theory. Frankly, the title pretty much tells you his conclusion “The ecological literature: an idea free distribution”, but if you want to know more, either read the original piece (it’s short) or EEB & Flow’s nice write up on it.  The empirical-theoretical divide is a topic I’ve been pondering for a while.

I have to admit I’m a superhero movie junkie. In particular, we watch the Avengers movie a lot in our household. I mean… a lot. Sometimes I really wish I was Natasha Romanoff (aka the Black Widow) from the Avengers. That would be rad. I could use my tricky spy interrogation skills to get program officers to tell me how to alter my proposal to get funded.

Barely a fortnight has passed since Science Magazine published the outcomes of a hoax perpetrated by one of their reporters, John Bohannon. Not surprisingly, the news article was widely criticized, not the least on this obscure blog. The content was simple enough: Bohannon picked a swath of largely fake journals, submitted fake manuscripts and boasted that more than 60% of his submissions were accepted.

This year’s Winter Conference on Animal Learning and Behavior (WCALB) will be on one of my oldest and most central research projects, the commonalities and differences between operant and classical conditioning.