This data set provides the datasets generated by the three creators (data challenge organisers) and subsequently provided to the participants of the EVA 2023 Data Challenge. 

The dataset aims to capture the variety of contexts experienced in the analysis of environmental extremes data. This involves both univariate and multivariate problems. The univariate extremes problems involve inference for extreme quantiles when faced with additional complications such as covariates; data missing at random; and the need to convert the inference into design levels which account for different losses from over- and under-design. 

The data set consists of five data files:
1. Amaurot: Training data given to the participants for Tasks 1 and 2
2. AmaurotTestSet: Collection of test data points for which predictions had to be submitted
3. Coputopia: Data participants had to consider for Task 3
4. UtopulaU1 + UtopulaU2: Data participants had to consider for Task 4

The aim of this dataset, developed for the Data Challenge, is to assess performance in multivariate extremes in a way that is independent of marginal extremes abilities. Consequently, the multivariate problems relate to data where the univariate marginal distributions are all known.