The original datasets are cleaned and processed to attain the displacement of each trip according to the origin and destination locations.
Here we present the original datasets and technical details during the execution, analysis, and interpretation of these omics studies.
In this system, some data sampling techniques are first used to generate different training subsets from the original datasets.
A major finding is the inverse relationship between the accuracy of RK models and the variation of soil properties in the original datasets.
We show that the global calibration transforms the original datasets towards a consensus that is hydrologically more coherent, with a budget residual reduced by 26%.
The original datasets includes 20 and 6 classes respectively.
The same LiDAR metrics as for the original datasets were derived from the thinned datasets.
The noise is added to the original datasets, and the noisy datasets are then saved.
This approach allows in particular the evaluation of the quality of data models wrt. the original datasets.
partners, are not classified (in the original datasets) and that the Social Innovation action area does not include any project.
We show both the correlations calculated from the original datasets and the mean of correlations calculated using the bootstrapping method.
