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.
\n\nThis deposit contains:
\n\n1. Training data: CSV dataset + 38 subfolders with data for each species (named after its GBIF id)
\n\n2. Prediction data:
\n\n2.1. Global use case (solstices & equinoxes of 2021): CSV dataset + data folder
\n\n2.2. Western Indian Ocean use case: CSV dataset + data folder
\n\n3. species.csv contains the taxonomic name of each taxon, as well as its GBIF id.
\n\n4. stats.npy contains normalization factors for the data files
\n\nmeds, perc1, perc99 = np.load(\"stats.npy\")\nitem = np.load(file)[:,:,:25]\nreal_values = (perc99 - perc1) * item + perc1\n\n
Each of these elements can be downloaded separately by scrolling to the Files section.
", "publication_date": "2023-07-27", "publisher": "Zenodo", "related_identifiers": [ { "identifier": "10.1101/2023.08.11.551418", "relation_type": { "id": "isdescribedby", "title": { "de": "Wird beschrieben von", "en": "Is described by" } }, "resource_type": { "id": "publication-preprint", "title": { "de": "Preprint", "en": "Preprint" } }, "scheme": "doi" } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "deep learning" }, { "subject": "megafauna" }, { "subject": "open oceans" }, { "subject": "pelagic species" }, { "subject": "species distribution models" } ], "title": "Deep-SDMs in the open oceans - INPUT DATA" }, "parent": { "access": { "owned_by": { "user": "335934" } }, "communities": { "default": "847d19e5-9891-403c-8120-d5d5f8aa208a", "entries": [ { "access": { "member_policy": "open", "members_visibility": "public", "record_submission_policy": "open", "review_policy": "open", "visibility": "public" }, "children": { "allow": false }, "created": "2023-01-20T17:06:54.349018+00:00", "custom_fields": { "subjects": [ { "id": "euroscivoc:317" }, { "id": "euroscivoc:489" }, { "id": "euroscivoc:323" } ] }, "deletion_status": { "is_deleted": false, "status": "P" }, "id": "847d19e5-9891-403c-8120-d5d5f8aa208a", "links": {}, "metadata": { "curation_policy": "New uploads in this community must contain publicly distributable material related to the activities of the UMR MARBEC and its partners
", "organizations": [ { "id": "049dk3691" }, { "id": "05q3vnk25" }, { "id": "00wh4bp03" }, { "id": "edmo:2895" }, { "id": "edmo:1054" } ], "page": "UMR MARBEC (MARine Biodiversity, Exploitation & Conservation) is a joint research unit that brings together staff from four main institutions, IRD, Ifremer, the University of Montpellier and CNRS, and a secondary institution, INRAE.
\n\nUMR MARBEC working on marine biodiversity exploitation and conversion. More details on www.umr-marbec.fr
\n\nThis ZENODO community contains open access material published by the UMR MARBEC members, such as raw data, reports, white papers, presentations, videos, codes, etc.
", "title": "MARBEC - MARine Biodiversity, Exploitation & Conservation", "type": { "id": "organization" }, "website": "https://www.umr-marbec.fr" }, "revision_id": 14, "slug": "umr-marbec", "updated": "2025-02-13T12:29:37.839338+00:00" } ], "ids": [ "847d19e5-9891-403c-8120-d5d5f8aa208a" ] }, "id": "8188512", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.8188512", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.8188513", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:8188513", "provider": "oai" } }, "revision_id": 5, "stats": { "all_versions": { "data_volume": 2525164346636.0, "downloads": 499, "unique_downloads": 431, "unique_views": 149, "views": 163 }, "this_version": { "data_volume": 2525164346636.0, "downloads": 499, "unique_downloads": 431, "unique_views": 149, "views": 163 } }, "status": "published", "swh": {}, "updated": "2023-08-21T13:11:05.377155+00:00", "versions": { "index": 1, "is_latest": true } }