Let's remind EMSLIBS 2019 conference in Brno a bit. As a part of the program, we have prepared challenging benchmark dataset, which was used for the classification contest. An extensive report could be found in our recent paper, but for highlights, you are just at the right place.

Unsurprisingly, the spectroscopic community was not missed out by the recent boom of modern machine learning approaches to data processing. Thus, we felt it was an ideal time to push the limits, compare performance, and also share knowledge across the community.

In our previous post, we described the common characteristics of spectroscopic data (sparsity, redundancy, and high dimensionality). In this post, we describe a dataset that you can use to experiment with spectroscopic data and to get familiar with the challenges posed by its properties. In the following, we state the motivation behind the creation of the dataset and its properties.