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Filtrado por: Fecha de creacion 2020-07 Eliminar la restricciónFecha de creacion: 2020-07 Departamento Research & Data Services, Data Scien Eliminar la restricciónDepartamento: Research & Data Services, Data Scien

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COVID 19 literature NLP models – Viral outbreak topic tuning

Type:

Dataset

Descripción/Resumen:

The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.

Creador/Autor:

Koshoffer, Amy; Wu, Danny; Latessa, Jenny; Kannayyagar, Suraj; Luken, Sally; McCabe, Erin; Edgerton, Ezra; Washington, Dorcas; Lee, James; Powers, Margaret, and Hagedorn, Philip

Peticionario:

Amy Koshoffer

Fecha modificada:

10/30/2020

Fecha modificada:

11/05/2020

Fecha de creacion:

2020-07

Licencia:

CC0 1.0 Universal
COVID 19 literature NLP models – Viral outbreak models formatted

Type:

Dataset

Descripción/Resumen:

The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.

Creador/Autor:

Koshoffer, Amy; Hagedorn, Philip ; Latessa, Jenny; Lee, James; Power, Margaret; Luken, Sally; McCabe, Erin; Wu. Danny; Washington, Dorcas; Kannayyagar, Suraj, and Edgerton, Ezra

Peticionario:

Amy Koshoffer

Fecha modificada:

10/29/2020

Fecha modificada:

11/05/2020

Fecha de creacion:

2020-07

Licencia:

CC0 1.0 Universal
COVID 19 literature NLP models - Viral outbreak model centrality files

Type:

Dataset

Descripción/Resumen:

The data sets were derived from coronavirus related scientific literature using the CORD-19 dataset released by the Allen Institute of Artificial Intelligence as of July 14, 2020, using the Elasticsearch engine hosted by the Digital Scholarship Center (DSC). Through indexing the full-text and the metadata of the article corpus, the research team generated a full-corpus model and 7 different models corresponding to key viral outbreaks from the past several decades' coronaviruses (SARS-CoV, MERS-CoV, and SARS- CoV-2) and non-coronaviruses (HIV, Zika, H1N1, and Ebola). The targeted subsets of the articles used two or more occurrences of virus-specific keywords drawn from conventions established by the World Health Organization.

Creador/Autor:

Koshoffer, Amy; Wu, Danny; Latessa, Jenny; Lee, James; Luken, Sally; McCabe, Erin; Edgerton, Ezra; Washington, Dorcas; Kannayyagar, Suraj, and Powers, Margaret

Peticionario:

Amy Koshoffer

Fecha modificada:

10/29/2020

Fecha modificada:

11/05/2020

Fecha de creacion:

2020-07

Licencia:

CC0 1.0 Universal

Limite su búsqueda

Tipo de trabajo

Dataset3

Creador

Edgerton, Ezra3
Kannayyagar, Suraj3
Koshoffer, Amy3
Latessa, Jenny3
Lee, James3
más Creadors »

Tema

COVID 193
Natural Language Processing3
Viruses2
viruses1

Colegio

Libraries3

Idioma

English3

Editor

University of Cincinnati3

Fecha de creacion

2020-07[remove]3

Colecciones

Data Sets for HDSR publication "Convergence in Viral Epidemic Research: Using Natural Language Processing to Define Network Bridges in the Bench-Bedside-Population Paradigm" 3

Scholar@UC Términos de uso

Deployed on 04-30-2026 06:39:34 PM

Información de derechos de autor & copy; 2026 University of Cincinnati

Preocupación de la accesibilidad

Aviso de Clery y HEOA

Aviso de no discriminación

Basado en Hyrax

Hecho posible por el proyecto Samvera