Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
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
_version_ 1784686510757380096
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
work_keys_str_mv AT wahltinezoscar covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT cheungaurora covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT alcantararuth covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT cheungdonny covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT daswanimayank covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT erlingeranthony covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT leematt covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT yawalkarpranali covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT lepaula covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT navarroofirpicazo covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT brennermichaelp covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease
AT murphykevin covid19opendataaglobalscalespatiallygranularmetadatasetforcoronavirusdisease