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COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease
This paper introduces the COVID-19 Open Dataset (COD), available at goo.gle/covid-19-open-data. A static copy is of the dataset is also available at 10.6084/m9.figshare.c.5399355. This is a very large “meta-dataset” of COVID-related data, containing epidemiological information, from 22,579 unique lo...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005692/ https://www.ncbi.nlm.nih.gov/pubmed/35413965 http://dx.doi.org/10.1038/s41597-022-01263-z |
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author | Wahltinez, Oscar Cheung, Aurora Alcantara, Ruth Cheung, Donny Daswani, Mayank Erlinger, Anthony Lee, Matt Yawalkar, Pranali Lê, Paula Navarro, Ofir Picazo Brenner, Michael P. Murphy, Kevin |
author_facet | Wahltinez, Oscar Cheung, Aurora Alcantara, Ruth Cheung, Donny Daswani, Mayank Erlinger, Anthony Lee, Matt Yawalkar, Pranali Lê, Paula Navarro, Ofir Picazo Brenner, Michael P. Murphy, Kevin |
author_sort | Wahltinez, Oscar |
collection | PubMed |
description | This paper introduces the COVID-19 Open Dataset (COD), available at goo.gle/covid-19-open-data. A static copy is of the dataset is also available at 10.6084/m9.figshare.c.5399355. This is a very large “meta-dataset” of COVID-related data, containing epidemiological information, from 22,579 unique locations within 232 different countries and independent territories. For 62 of these countries we have state-level data, and for 23 of these countries we have county-level data. For 15 countries, COD includes cases and deaths stratified by age or sex. COD also contains information on hospitalizations, vaccinations, and other relevant factors such as mobility, non-pharmaceutical interventions and static demographic attributes. Each location is tagged with a unique identifier so that these different types of information can be easily combined. The data is automatically extracted from 121 different authoritative sources, using scalable open source software. This paper describes the format and construction of the dataset, and includes a preliminary statistical analysis of its content, revealing some interesting patterns. |
format | Online Article Text |
id | pubmed-9005692 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90056922022-04-27 COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease Wahltinez, Oscar Cheung, Aurora Alcantara, Ruth Cheung, Donny Daswani, Mayank Erlinger, Anthony Lee, Matt Yawalkar, Pranali Lê, Paula Navarro, Ofir Picazo Brenner, Michael P. Murphy, Kevin Sci Data Data Descriptor This paper introduces the COVID-19 Open Dataset (COD), available at goo.gle/covid-19-open-data. A static copy is of the dataset is also available at 10.6084/m9.figshare.c.5399355. This is a very large “meta-dataset” of COVID-related data, containing epidemiological information, from 22,579 unique locations within 232 different countries and independent territories. For 62 of these countries we have state-level data, and for 23 of these countries we have county-level data. For 15 countries, COD includes cases and deaths stratified by age or sex. COD also contains information on hospitalizations, vaccinations, and other relevant factors such as mobility, non-pharmaceutical interventions and static demographic attributes. Each location is tagged with a unique identifier so that these different types of information can be easily combined. The data is automatically extracted from 121 different authoritative sources, using scalable open source software. This paper describes the format and construction of the dataset, and includes a preliminary statistical analysis of its content, revealing some interesting patterns. Nature Publishing Group UK 2022-04-12 /pmc/articles/PMC9005692/ /pubmed/35413965 http://dx.doi.org/10.1038/s41597-022-01263-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Wahltinez, Oscar Cheung, Aurora Alcantara, Ruth Cheung, Donny Daswani, Mayank Erlinger, Anthony Lee, Matt Yawalkar, Pranali Lê, Paula Navarro, Ofir Picazo Brenner, Michael P. Murphy, Kevin COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease |
title | COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease |
title_full | COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease |
title_fullStr | COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease |
title_full_unstemmed | COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease |
title_short | COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease |
title_sort | covid-19 open-data a global-scale spatially granular meta-dataset for coronavirus disease |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005692/ https://www.ncbi.nlm.nih.gov/pubmed/35413965 http://dx.doi.org/10.1038/s41597-022-01263-z |
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