Datasets for "Unexplored Antarctic meteorite collection sites revealed through machine learning"
This archive provides datasets related to the following publication: V. Tollenaar, H. Zekollari, S. Lhermitte, D. Tax, V. Debaille, S. Goderis, P. Claeys, F. Pattyn, Unexplored Antarctic meteorite collection sites revealed through machine learning. Science Advances 8, eabj8138 (2022). DOI:10.1126/sc...
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Online Access: | https://doi.org/10.5281/zenodo.5749753 |
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ftzenodo:oai:zenodo.org:5749753 2024-09-15T17:41:01+00:00 Datasets for "Unexplored Antarctic meteorite collection sites revealed through machine learning" Tollenaar, Veronica Zekollari, Harry Lhermitte, Stef Tax, David M.J. Debaille, Vinciane Goderis, Steven Claeys, Philippe Pattyn, Frank 2021-12-02 https://doi.org/10.5281/zenodo.5749753 eng eng Zenodo https://zenodo.org/communities/amgclabpublications https://doi.org/10.5281/zenodo.5749752 https://doi.org/10.5281/zenodo.5749753 oai:zenodo.org:5749753 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5281/zenodo.574975310.5281/zenodo.5749752 2024-07-26T12:53:45Z This archive provides datasets related to the following publication: V. Tollenaar, H. Zekollari, S. Lhermitte, D. Tax, V. Debaille, S. Goderis, P. Claeys, F. Pattyn, Unexplored Antarctic meteorite collection sites revealed through machine learning. Science Advances 8, eabj8138 (2022). DOI:10.1126/sciadv.abj8138 Contact: Veronica Tollenaar, Veronica.Tollenaar@ulb.be Users should cite the original publication when using all or part of the data. About the datasets: it includes a shapefile with the outline of the 613 Meteorite Stranding Zones (Fig. 7, "613MSZs.zip"), the observations used for classification, and the continent-wide probability to find meteorites (at 450-meter resolution, Fig. 5, "positive_classified.nc"). References to the literature are provided in the corresponding publication. Meteorite locations are based on the Meteoritical Bulletin Database (available at https://www.lpi.usra.edu/meteor/). - bias_above200m1kmbuff_expanded_dissolved: shapefile of polygons of unlabelled observations - meteorite_locations_raw.csv: contains locations of meteorite finds as defined in the meteoritical bulletin consulted on 05/07/2019 - meteorite_types.csv: contains meteorite names and types as defined in the meteoritical bulletin consulted on 05/07/2019 - validation_neg.csv: contains locations of negative observations used for validation - TEST_neg.csv: contains locations of negative test observations - TEST_pos.csv: contains locations of positive test obesrvations - MSZs_ranked: shapefile of ranked meteorite stranding zones - Test_neg4326: shapefile of locations used as negative test data - Cal_neg4326: shapefile of locations used as negative calibration/validation data - TestMSZs_pos4326: shapefile of locations used as positive test data in MSZ-level assesment - 613MSZs: shapefile of outlines of meteorite stranding zones - positive_classified.nc: netcdf of positive classified observations with their estimated a posteriori probabilities Other/Unknown Material Antarc* Antarctic Zenodo |
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Open Polar |
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Zenodo |
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ftzenodo |
language |
English |
description |
This archive provides datasets related to the following publication: V. Tollenaar, H. Zekollari, S. Lhermitte, D. Tax, V. Debaille, S. Goderis, P. Claeys, F. Pattyn, Unexplored Antarctic meteorite collection sites revealed through machine learning. Science Advances 8, eabj8138 (2022). DOI:10.1126/sciadv.abj8138 Contact: Veronica Tollenaar, Veronica.Tollenaar@ulb.be Users should cite the original publication when using all or part of the data. About the datasets: it includes a shapefile with the outline of the 613 Meteorite Stranding Zones (Fig. 7, "613MSZs.zip"), the observations used for classification, and the continent-wide probability to find meteorites (at 450-meter resolution, Fig. 5, "positive_classified.nc"). References to the literature are provided in the corresponding publication. Meteorite locations are based on the Meteoritical Bulletin Database (available at https://www.lpi.usra.edu/meteor/). - bias_above200m1kmbuff_expanded_dissolved: shapefile of polygons of unlabelled observations - meteorite_locations_raw.csv: contains locations of meteorite finds as defined in the meteoritical bulletin consulted on 05/07/2019 - meteorite_types.csv: contains meteorite names and types as defined in the meteoritical bulletin consulted on 05/07/2019 - validation_neg.csv: contains locations of negative observations used for validation - TEST_neg.csv: contains locations of negative test observations - TEST_pos.csv: contains locations of positive test obesrvations - MSZs_ranked: shapefile of ranked meteorite stranding zones - Test_neg4326: shapefile of locations used as negative test data - Cal_neg4326: shapefile of locations used as negative calibration/validation data - TestMSZs_pos4326: shapefile of locations used as positive test data in MSZ-level assesment - 613MSZs: shapefile of outlines of meteorite stranding zones - positive_classified.nc: netcdf of positive classified observations with their estimated a posteriori probabilities |
format |
Other/Unknown Material |
author |
Tollenaar, Veronica Zekollari, Harry Lhermitte, Stef Tax, David M.J. Debaille, Vinciane Goderis, Steven Claeys, Philippe Pattyn, Frank |
spellingShingle |
Tollenaar, Veronica Zekollari, Harry Lhermitte, Stef Tax, David M.J. Debaille, Vinciane Goderis, Steven Claeys, Philippe Pattyn, Frank Datasets for "Unexplored Antarctic meteorite collection sites revealed through machine learning" |
author_facet |
Tollenaar, Veronica Zekollari, Harry Lhermitte, Stef Tax, David M.J. Debaille, Vinciane Goderis, Steven Claeys, Philippe Pattyn, Frank |
author_sort |
Tollenaar, Veronica |
title |
Datasets for "Unexplored Antarctic meteorite collection sites revealed through machine learning" |
title_short |
Datasets for "Unexplored Antarctic meteorite collection sites revealed through machine learning" |
title_full |
Datasets for "Unexplored Antarctic meteorite collection sites revealed through machine learning" |
title_fullStr |
Datasets for "Unexplored Antarctic meteorite collection sites revealed through machine learning" |
title_full_unstemmed |
Datasets for "Unexplored Antarctic meteorite collection sites revealed through machine learning" |
title_sort |
datasets for "unexplored antarctic meteorite collection sites revealed through machine learning" |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://doi.org/10.5281/zenodo.5749753 |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_relation |
https://zenodo.org/communities/amgclabpublications https://doi.org/10.5281/zenodo.5749752 https://doi.org/10.5281/zenodo.5749753 oai:zenodo.org:5749753 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.574975310.5281/zenodo.5749752 |
_version_ |
1810487082463789056 |