Deep-SDMs in the open oceans - INPUT DATA

This repository contains input files to train the Deep-SDM model described in the preprint Predicting species distributions in the open oceans with convolutional neural networks. This deposit contains: 1. Training data: CSV dataset + 38 subfolders with data for each species (named after its GBIF id) 2. Prediction data: 2.1. Global use case (solstices &amp; equinoxes of 2021): CSV dataset + data folder 2.2. Western Indian Ocean use case: CSV dataset + data folder 3. <em>species.csv </em>contains the taxonomic name of each taxon, as well as its GBIF id. 4. <em>stats.npy</em> contains normalization factors for the data files <pre><code class="language-python">meds, perc1, perc99 = np.load("stats.npy") item = np.load(file)[:,:,:25] real_values = (perc99 - perc1) * item + perc1</code></pre> Each of these elements can be downloaded separately by scrolling to the <em>Files</em> section.