Yes, we have a small team of data scientists working specifically on our database, in collaboration and consultation with our broader group of scientists.

Our comment about 'scaling' concerned the transfer of results obtained using small scale approaches such as tests conducted in test tubes, bottles, or small enclosures to entire ecosystems. These small-scale approaches are widely used because there is a high degree of control, they are inexpensive, and easier to replicate.

While these approaches have considerable value as exploratory tools, there are often problems extrapolating their results to natural ecosystems. This is because small scale systems lack important elements of natural ecosystems such as contact of water with lake sediments and the atmosphere or the influence of soils and vegetation that surrounds natural lakes and streams. Natural ecosystems are also subject to immigration and emigration of organisms from surrounding areas and it may take years for changes to take effect.

Most small-scale approaches are short-term and do not allow for these effects. This is why the ability to conduct whole-lake experiments at IISD-ELA is so important. Here, we can directly test the influence of human activities at the scale that usually is most important to society – the ecosystem.

It is true that machine learning requires large volumes of data. So the benefits of ML are constrained to the size of the datasets we are working with. Fortunately, we have been around for over 50 years and includes dozens of lakes, so we have lots of data to work with already. With that said, the newer sensors we are deploying now will be able to provide us with a higher volume and resolution of data than we’ve ever had before, and we are excited about the possibilities that machine learning offers to make sense of it all.