|
RelatedMaterial
|
destroy: {"material_type"=>"Code", "availability"=>nil, "link"=>"https://doi.org/10.5281/zenodo.7897830", "uri"=>"", "uri_type"=>"", "citation"=>"Tonks, A. Y. M. (2023). adtonks/WNV-GNN: v1.0.0 (v1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7897830 ", "dataset_id"=>2438, "selected_type"=>"Code", "datacite_list"=>"", "note"=>nil, "feature"=>nil}
|
2025-01-08T23:48:51Z
|
|
RelatedMaterial
|
destroy: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.48550/arXiv.2212.11367", "uri"=>"", "uri_type"=>"", "citation"=>"Tonks, A., Harris, T., Li, B., Brown, W., & Smith, R. (2022). Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2212.11367", "dataset_id"=>2438, "selected_type"=>"Article", "datacite_list"=>"", "note"=>nil, "feature"=>nil}
|
2025-01-08T23:48:51Z
|
|
RelatedMaterial
|
update: {"uri"=>[nil, ""], "uri_type"=>[nil, ""], "datacite_list"=>[nil, ""]}
|
2023-05-10T19:28:15Z
|
|
RelatedMaterial
|
update: {"uri"=>[nil, ""], "uri_type"=>[nil, ""], "datacite_list"=>[nil, ""]}
|
2023-05-10T19:28:15Z
|
|
Dataset
|
update: {"version_comment"=>[nil, ""], "subject"=>[nil, "Physical Sciences"]}
|
2023-05-10T19:28:15Z
|
|
RelatedMaterial
|
create: {"material_type"=>"Code", "availability"=>nil, "link"=>"https://doi.org/10.5281/zenodo.7897830", "uri"=>nil, "uri_type"=>nil, "citation"=>"Tonks, A. Y. M. (2023). adtonks/WNV-GNN: v1.0.0 (v1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7897830 ", "dataset_id"=>2438, "selected_type"=>"Code", "datacite_list"=>nil}
|
2023-05-05T01:02:25Z
|
|
RelatedMaterial
|
create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.48550/arXiv.2212.11367", "uri"=>nil, "uri_type"=>nil, "citation"=>"Tonks, A., Harris, T., Li, B., Brown, W., & Smith, R. (2022). Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2212.11367", "dataset_id"=>2438, "selected_type"=>"Article", "datacite_list"=>nil}
|
2023-05-05T01:02:25Z
|
|
Dataset
|
update: {"description"=>["This is the data used in the paper \"Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data\". Code from the Github repository https://github.com/adtonks/mosquito_GNN can be used with the data here to reproduce the paper's results.", "This is the data used in the paper \"Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data\". A preprint may be found at https://doi.org/10.48550/arXiv.2212.11367\r\n\r\nCode from the Github repository https://github.com/adtonks/mosquito_GNN can be used with the data here to reproduce the paper's results. v1.0.0 of the code is also archived at https://doi.org/10.5281/zenodo.7897830"]}
|
2023-05-05T01:02:25Z
|
