Up until a few months ago, when people asked what the advantages of Julia were, I usually mentioned its speed, maintainability, and how easy it was to run your code in a distributed way. Now, I usually add that Chris Rackauckas created the best differential equations package available (DifferentialEquations.jl ). Check out the comparison with other packages. I thought it would be useful to give a brief overview of one very useful feature it has when solving ecological problems.
In what is going to be the most technical note so far, I will try to reflect on a few years of using the Julia programming language for computational ecology projects. In particular, I will discuss how multiple dispatch changed my life (for the better), and how it can be used to make ecological analyses streamlined. I will most likely add a few entries to this series during the fall, leading up to a class I will give in the winter.
In this follow-up to the previous part of the manuscript on computational ecology, I explore some of the ways to facilitate collaborations between data users and data producers. You can read the first part to get up to speed, and then feel free to comment and give feedback.
What is computational ecology? I am working on a manuscript to discuss this topic in the context of ecological synthesis. Since it is almost ready, I would love to get some feedback. And so I have pasted below a part of the introduction. If this little experiment goes well, I will add another section next week, before making it public as a preprint. Think of this as a trailer. To an academic article. Because these, I guess, are the times we live in.
Every time I hear about Big Data in ecology, I cringe a little bit. Some of us may be lucky enough to have genuinely big data, but I believe this is the exception rather than the norm. And this is a good thing, because tiny data are extremely exciting – in short, they offer the challenge of isolating a little bit of signal in a lot of noise, and this is a perfect excuse to apply some really fun tools. And one of my favorite approaches for really small data is ABC, Approximate Bayesian Computation. Let’s dig in!