cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4957846
title: GeoVectors-Japan-location (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 location-embeddings for the region "Japan" including the following countries: Japan <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.4323105
title: GeoVectors-Japan-location (v0.1)

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 location-embeddings for the region "Japan" including the following countries: Japan <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.4323161
title: GeoVectors-North-America-location (v0.1)

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 location-embeddings for the region "North-America" including the following countries: Canada Greenland Mexico <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.4957845
title: GeoVectors-Japan-location (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 location-embeddings for the region "Japan" including the following countries: Japan <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.4323104
title: GeoVectors-Japan-location (v0.1)

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 location-embeddings for the region "Japan" including the following countries: Japan <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.4957840
title: GeoVectors-Italy-location (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 location-embeddings for the region "Italy" including the following countries: Italy <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.4323088
title: GeoVectors-Italy-location (v0.1)

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 location-embeddings for the region "Italy" including the following countries: Italy <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.4957839
title: GeoVectors-Italy-location (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 location-embeddings for the region "Italy" including the following countries: Italy <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.4323074
title: GeoVectors-Indonesia-location (v0.1)

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 location-embeddings for the region "Indonesia" including the following countries: Indonesia <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.4323087
title: GeoVectors-Italy-location (v0.1)

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 location-embeddings for the region "Italy" including the following countries: Italy <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.4957818
title: GeoVectors-Indonesia-location (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 location-embeddings for the region "Indonesia" including the following countries: Indonesia <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.4323073
title: GeoVectors-Indonesia-location (v0.1)

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 location-embeddings for the region "Indonesia" including the following countries: Indonesia <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).