Last week Zack Brym and I formally announced a semester long Data Carpentry course that we’ve have been building over the last year. One of the things I’m most excited about in this effort is our attempt to support collaborative lesson development for university/college coursework.

This is post is co-authored by Zack Brym and Ethan White Over the last year and a half we have been actively developing a semester-long Data Carpentry course designed to be easily customized and integrated into existing graduate and undergraduate curricula. Data Carpentry for Biologists contains course materials for teaching scientists how to work more effectively with data.

I’m looking for one or more graduate students to join my group next fall. In addition to the official add (below) I’d like to add a few extra thoughts. As Morgan Ernest noted in her recent ad, we have a relatively unique setup at Weecology in that we interact actively with members of the Ernest Lab. We share space, have joint lab meetings, and generally maintain a very close intellectual relationship.

This is a guest post by Elita Baldridge (@elitabaldridge), a graduate student in Ethan White’s lab in the Ecology Center at Utah State University. As a budding macroecologist, I have thought a lot about what skills I need to acquire during my Ph.D. This is my model of the four basic attributes for a macroecologist, although I think it is more generally applicable to many ecologists as well: Data Statistics Math

Slides and script from Ethan White’s Ignite talk on Big Data in Ecology from Sandra Chung and Jacquelyn Gill‘s excellent ESA 2013 session on Sharing Makes Science Better. Slides are also archived on figshare. 1.  I’m here to talk to you about the use of big data in ecology and to help motivate a lot of the great tools and approaches that other folks will talk about later in the session. 2.

Figuring out how to teach well as a professor at a research university is largely a self-study affair. For me the keys to productive self-study are good information and self-reflection. Without good information you’re not learning the right things and without self-reflection you don’t know if you are actually succeeding at implementing what you’ve learned.

Some time ago in academia we realized that it didn’t make sense for individual scientists or even entire departments to maintain their own high performance computing resources.

An increasingly large number of folks doing research in ecology and other biological disciplines spend a substantial portion of their time writing computer programs to analyze data and simulate the outcomes of biological models. However, most ecologists have little formal training in software development¹. A recent survey suggests that we are not only;

As I’ve mentioned before I’m not a big fan of the configuration of most comprehensive exams, but my post on the matter keeps languishing on my out of control To Do list.

Anyone who has been around the halls of academia for a while has heard some well meaning soul talk about how we produce too many PhD students for the number of faculty positions, that this is unfair, and that therefore we should take fewer students. The most recent version of this idea on the web goes so far as calling the academic enterprise a Ponzi scheme. I’ve never personally found this argument very convincing.