Analysis of Bio-Oracle data

Objective

While running some brief quality control tests on Bio-Oracle layers before using them for a recent project it was detected that some of the layers in the current version of the Bio-Oracle product appear to have very large errors. Specifically the error is that there are layers where the minimum values are greater than the maximum values. It is unclear how this could be possible, so in the following text and code we will look into how we go about investigating these data layers and we will discuss which layers are fine, and which are not. This error was first detected in the current velocity layers but a brief search turned up errors in other layers, too. So in this post we will be going through each individual layer to test for this max less than min error. We will look at all of the different depths as well as the future projections.

Downloading environmental data in R

Objective

Having been working in environmental science for several years now, entirely using R, I’ve come to greatly appreciate environmental data sources that are easy to access. If you are reading this text now however, that probably means that you, like me, have found that this often is not the case. The struggle to get data is real. But it shouldn’t be. Most data hosting organisations do want scientists to use their data and do make it freely available. But sometimes it feels like the path to access was designed by crab people, rather than normal topside humans. I recently needed to gather several new data products and in classic ‘cut your nose off to spite your face’ fashion I insisted on doing all of it directly through an R script that could be run in RStudio. Besides being stubborn, one of the main reasons I felt this was necessary is that I wanted these download scripts to be able to be run operationally via a cron job. I think I came out pretty successful in the end so wanted to share the code with the rest of the internet. Enjoy.