Dataset
COVID 19 literature NLP models - Viral outbreak model centrality files Acceso Abierto Deposited
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.
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Elementos
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H1N1_centrality_topics_excl_covid19.csv | 2020-10-29 | Acceso Abierto |
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HIV_centrality_topics.csv | 2020-10-29 | Acceso Abierto |
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HIV_centrality_topics_excl_covid19.csv | 2020-10-29 | Acceso Abierto |
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MERS_centrality_topics.csv | 2020-10-29 | Acceso Abierto |
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MERS_centrality_topics_excl_covid19.csv | 2020-10-29 | Acceso Abierto |
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SARS_centrality_topics.csv | 2020-10-29 | Acceso Abierto |
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SARS_centrality_topics_excl_covid-19.csv | 2020-10-29 | Acceso Abierto |
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Zika_centrality_topics.csv | 2020-10-29 | Acceso Abierto |
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Zika_centrality_topics_excl_covid19.csv | 2020-10-29 | Acceso Abierto |
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Enlace permanente a esta página: https://scholar.uc.edu/show/6t053h21x