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Filtrado por: Idioma English Eliminar la restricciónIdioma: English 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" Eliminar la restricciónColecciones: Data Sets for HDSR publication "Convergence in Viral Epidemic Research: Using Natural Language Processing to Define Network Bridges in the Bench-Bedside-Population Paradigm"

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  • APPENDIX D: Topic Network Maps for a Random 10k Documents & the Complete CORD-19 Dataset

    Document thumbnail: APPENDIX D: Topic Network Maps for a Random 10k Documents & the Complete CORD-19 Dataset
    Type:
    Document
    Descripción/Resumen:
    All models and corresponding network visualizations are generated from documents in the CORD-19 dataset as of July 14, 2020. All annotations in red were added by the research team. Note: These topic models are included here as additional reference and to append links to interactive versions on the Digital Scholarship Center’s machine learning platform for further exploration.
    Creador/Autor:
    McCabe, Erin E.
    Peticionario:
    Erin E. McCabe
    Fecha modificada:
    02/26/2021
    Fecha modificada:
    02/26/2021
    Fecha de creacion:
    2020
    Licencia:
    CC0 1.0 Universal

  • APPENDIX C: Non-Coronaviridae Topic Network Maps [HIV, ZIKA, H1N1, EBOLA]

    Document thumbnail: APPENDIX C: Non-Coronaviridae Topic Network Maps [HIV, ZIKA, H1N1, EBOLA]
    Type:
    Document
    Descripción/Resumen:
    All models and corresponding network visualizations are generated from virus related documents in the CORD-19 dataset as of July 14, 2020. All annotations in red were added by the research team. Note: Certain Non-Coronaviridae topic models are included in the text of this article and are included here only as additional reference and to append links to interactive versions on the Digital Scholarship Center’s machine learning platform for further exploration.
    Creador/Autor:
    McCabe, Erin E.
    Peticionario:
    Erin E. McCabe
    Fecha modificada:
    02/26/2021
    Fecha modificada:
    02/26/2021
    Fecha de creacion:
    2020
    Licencia:
    CC0 1.0 Universal

  • APPENDIX B: Coronaviridae Topic Network Maps [SARS, MERS, COVID-19]

    Document thumbnail: APPENDIX B: Coronaviridae Topic Network Maps [SARS, MERS, COVID-19]
    Type:
    Document
    Descripción/Resumen:
    All models and corresponding network visualizations are generated from virus related documents in the CORD-19 dataset as of July, 2020. All annotations in red were added by the research team. Note: Coronavirus topic models are included in the text of this article and are included here only as additional reference and to append links to interactive versions on the Digital Scholarship Center’s machine learning platform for further exploration.
    Creador/Autor:
    McCabe, Erin E.
    Peticionario:
    Erin E. McCabe
    Fecha modificada:
    02/26/2021
    Fecha modificada:
    02/26/2021
    Licencia:
    CC0 1.0 Universal

  • COVID 19 literature NLP models – Viral outbreak topic tuning

    Dataset thumbnail: 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

    Dataset thumbnail: 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

    Dataset thumbnail: 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