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/s...
Main Authors: | , , , , , , , |
---|---|
Format: | Dataset |
Language: | English |
Published: |
Zenodo
2021
|
Subjects: | |
Online Access: | https://dx.doi.org/10.5281/zenodo.5749753 https://zenodo.org/record/5749753 |
id |
ftdatacite:10.5281/zenodo.5749753 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.5281/zenodo.5749753 2023-05-15T13:33:45+02:00 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 https://dx.doi.org/10.5281/zenodo.5749753 https://zenodo.org/record/5749753 en eng Zenodo https://dx.doi.org/10.5281/zenodo.5749752 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Dataset dataset 2021 ftdatacite https://doi.org/10.5281/zenodo.5749753 https://doi.org/10.5281/zenodo.5749752 2022-03-10T10:41:55Z 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 Dataset Antarc* Antarctic DataCite Metadata Store (German National Library of Science and Technology) Antarctic |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
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 |
Dataset |
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://dx.doi.org/10.5281/zenodo.5749753 https://zenodo.org/record/5749753 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_relation |
https://dx.doi.org/10.5281/zenodo.5749752 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.5749753 https://doi.org/10.5281/zenodo.5749752 |
_version_ |
1766045595145863168 |