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Filtrado por: Creador Hagedorn, Philip Editor University of Cincinnati

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