COVID 19 literature NLP models – Viral outbreak topic tuning

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

Dataset

COVID 19 literature NLP models – Viral outbreak topic tuning 开放存取 Deposited

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Date Uploaded: 10/30/2020
Date Modified: 11/05/2020

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.

创建者

证书

CC0 1.0 Universal

学科

提交

Amy Koshoffer

学

Libraries

部门

Research & Data Services, Data Scien

创建日期

2020-07

出版者

University of Cincinnati

语言

English

关系

在收集：

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

单件

标题	上传日期	公开度	行动
covid_q2_topics_tuning.csv	2020-10-30	开放存取	下载
ebola_m2_topics_tuning.csv	2020-10-30	开放存取	下载
10k_eval_avgs.txt	2020-10-30	开放存取	下载
covid_q1_topics_tuning.csv	2020-10-30	开放存取	下载
hiv_m2_topics_tuning.csv	2020-10-30	开放存取	下载
hyperparam_tuning_on_10k_rand.csv	2020-10-30	开放存取	下载
full_tuning_results.csv	2020-10-30	开放存取	下载
h1n1_m2_topics_tuning.csv	2020-10-30	开放存取	下载
zika_m2_topics_tuning.csv	2020-10-30	开放存取	下载
mers_m2_topics_tuning.csv	2020-10-30	开放存取	下载