So here is an important question: if you give the same dataset to different scientists, will the outcomes be the same?

This question is not trivial. It’s actually one of the most important assumptions in the way we currently do science, and thus the base of so much of our knowledge: any decent analysis is going to uncover the truths hiding in a dataset, whoever looked at it first.

However, this critical assumption is rarely tested, so we pretty much don’t know if this assumption actually holds! Let’s solve that, shall we? So I stumbled upon this fantastic initiative from Hannah Fraser and others (here) aimed at filling that void in our knowledge. Their plan is as simple as it is brilliant: just give exactly the same dataset to ecologists from all over the world, let them all analyse it as they would do for their own papers, and carefully compare the outcomes.

See, this is the kind of science that gets me excited: building on global collaboration, challenging the foundations of our scientific understanding, and building towards a better science in the future. So obviously we are playing along.

Our experimental questions will deal with how grass cover influences Eucalyptus seedling recruitment (yes, I have no relevant pictures for that, I have never been to Australia, but that doesn’t mean we can’t analyze the data!)

What is even better about this initiative, is the fact that we can make this a team excercise! We can bring our Virtual Lab together and all work as a team on a shared research question. It is the perfect opportunity to organize that ‘practical statistics and paper writing course’ that students often crave for in their masters or early PhD: get a dataset, and work your way through the whole process of analyzing and writing up the results, without the extra pressure you get when it is your thesis work and you are the lea author. Learning from each other, taking along the new students and having the established one lead the way. This is what our Virtual Lab was waiting for!

So let’s see where this brings us. We are looking forward to do this teambuilding excercise, and in the meantime contribute important knowledge to the scientific community. We’ll keep you posted…