COVID-19
Objectives: Worldwide efforts to protect front line providers performing endotracheal intubation during the COVID-19 pandemic have led to innovative devices. Authors evaluated the aerosol containment effectiveness of a novel intubation aerosol containment system (IACS) compared with a recently promoted intubation box and no protective barrier. Methods: In a simulation center at the authors’ university, the IACS was compared to no protective barrier and an intubation box.
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This data set includes Covid-19 related Tweet messages written in Turkish that contain at least one of four keywords (Covid, Kovid, Corona, Korona). These keywords are used to express Covid-19 virus in Turkey. Tweets collection was started from 11th March 2020, the first Covid-19 case seen in Turkey.
Currently dataset contain 4,8 million tweets with 6 different attribute of each tweets that were sent from 9 March 2020 until 6 May 2020.
The data file contains comma separated values (CSV). It contains the following information (6 Column) for each tweet in the data file:
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This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training.
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This dataset (GeoCOV19Tweets) contains IDs and sentiment scores of geo-tagged tweets related to the COVID-19 pandemic. The real-time Twitter feed is monitored for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. The tweet IDs in this dataset belong to the tweets created providing an exact location.
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The outbreak of COVID-19 in Wuhan, China in December 2019 has rapidly spread across other countries in the world and has been declared as a global pandemic by WHO on 11th March, 2020. COVID-19 continues to have adverse effects on the health and economy of the global population and has brought immense pressure on the health care systems of the developing as well as developed countries.
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Enrichment analysis performed by Enrichr toward genes associated with coronavirus infeciton
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This dataset (COV19Tweets) includes CSV files that contain IDs and sentiment scores of the tweets related to the COVID-19 pandemic. The real-time Twitter feed is monitored for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. The oldest tweets in this dataset date back to October 01, 2019. This dataset has been wholly re-designed on March 20, 2020, to comply with the content redistribution policy set by Twitter.
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