Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"

The tar file contains two directories: data and models.  Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data).  The data in these directories are unnormalized.  The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file.  These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively.  To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths.  The other arguments should be as follows:

--uniformize=1

--predictor_norm_type_string="z_score"

--vector_target_norm_type_string=""

--scalar_target_norm_type_string=""

Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data.  To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.