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Filtering by: Creator/Author Lee, James Remove constraint Creator/Author: Lee, James Department Research & Data Services, Data Scien Remove constraint Department: Research & Data Services, Data Scien

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COVID 19 literature NLP models – Viral outbreak topic tuning

Type:

Dataset

Description/Abstract:

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.

Creator/Author:

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

Submitter:

Amy Koshoffer

Date Uploaded:

10/30/2020

Date Modified:

11/05/2020

Date Created:

2020-07

License:

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

Type:

Dataset

Description/Abstract:

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.

Creator/Author:

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

Submitter:

Amy Koshoffer

Date Uploaded:

10/29/2020

Date Modified:

11/05/2020

Date Created:

2020-07

License:

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

Type:

Dataset

Description/Abstract:

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.

Creator/Author:

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

Submitter:

Amy Koshoffer

Date Uploaded:

10/29/2020

Date Modified:

11/05/2020

Date Created:

2020-07

License:

CC0 1.0 Universal

Limit your search

Type of Work

Dataset3

Creator/Author

Edgerton, Ezra3
Kannayyagar, Suraj3
Koshoffer, Amy3
Latessa, Jenny3
Lee, James[remove]3
more Creator/Authors »

Subject

COVID 193
Natural Language Processing3
Viruses2
viruses1

College

Libraries3

Language

English3

Publisher

University of Cincinnati3

Date Created

2020-073

Collections

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

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