cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4320868
title: GeoVectors-Antarctica-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic dimensions of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic dimension of OpenStreetMap entities. The "-location" datasets provide the geographic dimension. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Antarctica" including the following countries: Antarctica <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap ID of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4320881
title: GeoVectors-Africa-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Africa" including the following countries: Algeria Angola Benin Botswana Burkina-Faso Burundi Cameroon Canary-Islands Cape-Verde Central-African-Republic Chad Comores Congo-Brazzaville Congo-Democratic-Republic Djibouti Egypt Equatorial-Guinea Eritrea Ethiopia Gabon Ghana Guinea Guinea-Bissau Ivory-Coast Kenya Lesotho Liberia Libya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Namibia Niger Nigeria Rwanda Saint-Helena-Ascension-and-Tristan-Da-Cunha Sao-Tome-and-Principe Senegal-and-Gambia Seychelles Sierra-Leone Somalia South-Africa South-Sudan Sudan Swaziland Tanzania Togo Tunisia Uganda Zambia Zimbabwe <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4320880
title: GeoVectors-Africa-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Africa" including the following countries: Algeria Angola Benin Botswana Burkina-Faso Burundi Cameroon Canary-Islands Cape-Verde Central-African-Republic Chad Comores Congo-Brazzaville Congo-Democratic-Republic Djibouti Egypt Equatorial-Guinea Eritrea Ethiopia Gabon Ghana Guinea Guinea-Bissau Ivory-Coast Kenya Lesotho Liberia Libya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Namibia Niger Nigeria Rwanda Saint-Helena-Ascension-and-Tristan-Da-Cunha Sao-Tome-and-Principe Senegal-and-Gambia Seychelles Sierra-Leone Somalia South-Africa South-Sudan Sudan Swaziland Tanzania Togo Tunisia Uganda Zambia Zimbabwe <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4321010
title: GeoVectors-Central-America-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Central-America" including the following countries: Bahamas Belize Cuba Guatemala Haiti-and-Domrep Jamaica Nicaragua <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4321009
title: GeoVectors-Central-America-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Central-America" including the following countries: Bahamas Belize Cuba Guatemala Haiti-and-Domrep Jamaica Nicaragua <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4321012
title: GeoVectors-Europe-East-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Europe-East" including the following countries: Albania Belarus Bosnia-Herzegovina Bulgaria Croatia Cyprus Czech-Republic Estonia Finland Georgia Greece Hungary Iceland Kosovo Latvia Lithuania Macedonia Moldova Montenegro Romania Serbia Slovakia Slovenia Sweden Turkey Ukraine <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4321099
title: GeoVectors-Europe-West-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Europe-West" including the following countries: Andorra Austria Azores Belgium Denmark Faroe-Islands Guernsey-Jersey Ireland-and-Northern-Ireland Isle-Of-Man Liechtenstein Luxembourg Malta Monaco Norway Portugal Spain Switzerland <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4321011
title: GeoVectors-Europe-East-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Europe-East" including the following countries: Albania Belarus Bosnia-Herzegovina Bulgaria Croatia Cyprus Czech-Republic Estonia Finland Georgia Greece Hungary Iceland Kosovo Latvia Lithuania Macedonia Moldova Montenegro Romania Serbia Slovakia Slovenia Sweden Turkey Ukraine <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4321098
title: GeoVectors-Europe-West-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Europe-West" including the following countries: Andorra Austria Azores Belgium Denmark Faroe-Islands Guernsey-Jersey Ireland-and-Northern-Ireland Isle-Of-Man Liechtenstein Luxembourg Malta Monaco Norway Portugal Spain Switzerland <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4321153
title: GeoVectors-France-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "France" including the following countries: France <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).
DOI: 10.5281/zenodo.4321152
title: GeoVectors-France-tags (v1.0)

description: <strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "France" including the following countries: France <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).