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.

Instructions: 

Download and play video file

Categories:
404 Views

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:

Instructions: 

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.

Original CSV data file is zipped by WinRAR to upload and download easily. The zipped file size is 76 MB.

This data can be used for text mining such as topic modelling, sentiment analysis etc.

The data file contains comma separated values (CSV). It contains the following information (6 Column) for each tweet in the data file:

Created-At: Exact creation time of the tweet
From-User-Id: Sender User Id
To-User-Id: if it is sent to a user, its user ID
Language: All Turkish
Retweet-Count: number of retweets
Id: ID of tweet that is unique for all tweets

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3467 Views

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.

Instructions: 

This tool model propose a Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT architecture. Based on autoencoder of Mask-RCNN for area mark feature maps objection detection for the identification of COVID-19 pneumonia have very serious pathological and always accompanied by various of symptoms. We collect a lot of lung x-ray images were be integrated into DICM style dataset prepare for experiment on computer on vision algorithms, and deep learning architecture based on autoencoder of Mask- RCNN algorithms are the main technological breakthrough.

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2421 Views

This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed 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. You can re-construct the dataset by hydrating these IDs.

Instructions: 

Each CSV file contains a list of tweet IDs. You can use these tweet IDs to download fresh data from Twitter (hydrating the tweet IDs). To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score computed by TextBlob has been appended as the second column. To hydrate the tweet IDs, you can use applications such as Hydrator (available for OS X, Windows and Linux) or twarc (python library).

Getting the CSV files of this dataset ready for hydrating the tweet IDs:

import pandas as pd

dataframe=pd.read_csv("april28_april29.csv", header=None)

dataframe=dataframe[0]

dataframe.to_csv("ready_april28_april29.csv", index=False, header=None)

The above example code takes in the original CSV file (i.e., april28_april29.csv) from this dataset and exports just the tweet ID column to a new CSV file (i.e., ready_april28_april29.csv). The newly created CSV file can now be consumed by the Hydrator application for hydrating the tweet IDs. To export the tweet ID column into a TXT file, just replace ".csv" with ".txt" in the to_csv function (last line) of the above example code.

If you are not comfortable with Python and pandas, you can upload these CSV files to your Google Drive and use Google Sheets to delete the second column. Once finished with the deletion, download the edited CSV files: File > Download > Comma-separated values (.csv, current sheet). These downloaded CSV files are now ready to be used with the Hydrator app for hydrating the tweets IDs.

Categories:
24366 Views

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.

Instructions: 

Please refer the "Readme_CXR_Database_v1.0" for detailed instructions on how to use the dataset. 

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2373 Views

Diagnosis of Coronavirus Disease 2019 (COVID-19) Surveillance Using C4.5 Algorithm

Instructions: 

Laporan Penelitian Dosen Yayasan Semester Genap 2020

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1091 Views

As part of preparedness efforts in dealing with this, it is important for Indonesia to prepare guidelines for preparedness in dealing with COVID-19.

Instructions: 

This guideline is intended for health workers as a reference in preparing for COVID-19. This guideline is provisional because it has been prepared by adopting WHO interim guidelines so that it will be updated in accordance with disease developments and the current situation.

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2203 Views

Enrichment analysis performed by Enrichr toward genes associated with coronavirus infeciton

Instructions: 

Genes are selected by TD based unsupervised FE and are uploaded to Enrichr

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339 Views

Free dataset from news/message boards/blogs about CoronaVirus (4 month of data - 5.2M posts). The time frame of the data is Dec/2019 - March/2020. The posts are in English mentioning at least one of the following: "Covid" OR CoronaVirus OR "Corona Virus".

 

Instructions: 

The data is stored inside a zip file that contains a JSON file. Here is an example of a JSON post:

 

 

  • {
  • "organizations":[],
  • "uuid":"2b50b3f00e04fc17912154a7b88f3359db2b1ae8",
  • "thread":{
  • "social":{
  • "gplus":{
  • "shares":0
  • },
  • "pinterest":{
  • "shares":1
  • },
  • "vk":{
  • "shares":0
  • },
  • "linkedin":{
  • "shares":0
  • },
  • "facebook":{
  • "likes":19,
  • "shares":63,
  • "comments":7
  • },
  • "stumbledupon":{
  • "shares":0
  • }
  • },
  • "site_full":"www.foxnews.com",
  • "main_image":"https://cf-images.us-east-1.prod.boltdns.net/v1/static/694940094001/abd7...",
  • "site_section":"http://feeds.foxnews.com/foxnews/latest",
  • "section_title":"FOX News",
  • "url":"https://www.foxnews.com/media/dr-siegel-on-coronavirus-i-think-is-a-whop...",
  • "country":"US",
  • "domain_rank":185,
  • "title":"Dr. Marc Siegel on coronavirus: 'I think it is a whopping amount of cases undiagnosed'",
  • "performance_score":0,
  • "site":"foxnews.com",
  • "participants_count":1,
  • "title_full":"",
  • "spam_score":0.0,
  • "site_type":"news",
  • "published":"2020-03-14T04:20:00.000+02:00",
  • "replies_count":0,
  • "uuid":"2b50b3f00e04fc17912154a7b88f3359db2b1ae8"
  • },
  • "author":"Victor Garcia",
  • "url":"https://www.foxnews.com/media/dr-siegel-on-coronavirus-i-think-is-a-whop...",
  • "ord_in_thread":0,
  • "title":"Dr. Marc Siegel on coronavirus: 'I think it is a whopping amount of cases undiagnosed'",
  • "locations":[],
  • "entities":{
  • "persons":[{
  • "name":"marc siegel",
  • "sentiment":"negative"
  • },{
  • "name":"siegel",
  • "sentiment":"none"
  • },{
  • "name":"tucker carlson",
  • "sentiment":"none"
  • },{
  • "name":"trump",
  • "sentiment":"none"
  • },{
  • "name":"trump",
  • "sentiment":"none"
  • }],
  • "locations":[{
  • "name":"us",
  • "sentiment":"none"
  • }],
  • "organizations":[{
  • "name":"fox news",
  • "sentiment":"negative"
  • }]
  • },
  • "highlightText":"",
  • "language":"english",
  • "persons":[],
  • "text":"US doctors report inability to get tests for coronavirus patients Reaction from Fox News medical correspondent Dr. Marc Siegel. Dr. Marc Siegel appeared on \" Tucker Carlson Tonight \" on Friday where he gave his assessment of the coronavirus pandemic in the aftermath of President Trump declaring a national emergency. \"There's many thousands of cases that have not been diagnosed, possibly because they're mild, but it's not too late to test because we don't have another system we can work with,\" Siegel said.\nTRUMP DECLARES NATIONAL EMERGENCY OVER CORONAVIRUS, ENLISTS PRIVATE SECTOR\nSiegel also spoke about the problems hospitals and labs are having, fearing they are exposing their workers to the virus.\n\"Doctors are being told you don't see these patients. Well, we don't know what to do with them then. And the only thing we have is a test, except you can't actually do the test because the lab, and I just found out this today... they're not going to do the tests,\" Siegel said. \"Tucker, even if they have the equipment, they don't want to put their lab technicians, in my opinion, in the line of fire and be subjected to possible coronavirus.\"\nThe solution, according to the Fox News medical contributor, is to do what South Korea and facilities in Nebraska are doing -- drive-thru testing facilities.\n\"You have to have people dressed up in personal protective equipment like we showed in Nebraska. They have to be doing [it] very carefully. And it's got to be done on a high volume basis anyway,\" Siegel said. \"It can't be contained anymore. But I'll tell you why I want it done.\"\nCLICK HERE TO GET THE FOX NEWS APP\nSiegel said although the virus has already spread, testing is vital to \"reassure people who don't have it\" and \"decrease the panic.\"\n\"We have to know who has this so we can protect the people most at risk, even if it's sustained throughout all communities,\" Siegel said. \"I think it is a whopping amount of cases undiagnosed. We still need to know who has it.\" Get all the stories you need-to-know from the most powerful name in news delivered first thing every morning to your inbox Arrives Weekdays",
  • "external_links":["https://www.google.com/url","https://google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=12&cad=rja&uac...","https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=12&cad=rja..."],
  • "published":"2020-03-14T04:20:00.000+02:00",
  • "crawled":"2020-03-14T04:31:41.175+02:00",
  • "highlightTitle":""
  • }

 

Categories:
2483 Views

This dataset includes CSV files that contain IDs and sentiment scores of the tweets related to the COVID-19 pandemic. The tweets have been collected by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. This dataset has been wholly re-designed on March 20, 2020, to comply with the content redistribution policy set by Twitter.

Instructions: 

Each CSV file contains a list of tweet IDs. You can use these tweet IDs to download fresh data from Twitter (hydrating the tweet IDs). To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score computed by TextBlob has been appended as the second column. To hydrate the tweet IDs, you can use applications such as Hydrator (available for OS X, Windows and Linux) or twarc (python library).

Getting the CSV files of this dataset ready for hydrating the tweet IDs:

import pandas as pd

dataframe=pd.read_csv("corona_tweets_10.csv", header=None)

dataframe=dataframe[0]

dataframe.to_csv("ready_corona_tweets_10.csv", index=False, header=None)

The above example code takes in the original CSV file (i.e., corona_tweets_10.csv) from this dataset and exports just the tweet ID column to a new CSV file (i.e., ready_corona_tweets_10.csv). The newly created CSV file can now be consumed by the Hydrator application for hydrating the tweet IDs. To export the tweet ID column into a TXT file, just replace ".csv" with ".txt" in the to_csv function (last line) of the above example code.

If you are not comfortable with Python and pandas, you can upload these CSV files to your Google Drive and use Google Sheets to delete the second column. Once finished with the deletion, download the edited CSV files: File > Download > Comma-separated values (.csv, current sheet). These downloaded CSV files are now ready to be used with the Hydrator app for hydrating the tweets IDs.

Categories:
100435 Views

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