Depressive/Non-depressive tweets  between December 2019 and December 2020 originated largely from India and parts of Indian subcontinent. Sentiment Scores alloted using text blob. Tweets are extracted specifically keeping in mind the top 250 most frequently used negative lexicons and positive lexicons accesed using SentiWord and various research publications.

Tweet Amount : 1.4 Lakhs

 

Instructions: 

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668 Views

The CoVID19-FNIR dataset contains news stories related to CoVID-19 pandemic fact-checked by expert fact-checkers. CoVID19-FNIR is a CoVID-19-specific dataset consisting of fact-checked fake news scraped from Poynter and true news from the verified Twitter handles of news publishers. The data samples were collected from India, The United States of America, and European regions and consist of online posts from social media platforms between February 2020 to June 2020. The dataset went through prepossessing steps that include removing special characters and non-vital information.

Instructions: 

The CoVID19-FNIR.zip folder contains the whole dataset. The folder has two files; (1) fakeNews.csv, and (2) trueNews.csv. The data in .csv files contain the news article and the corresponding fake rating collected from the USA, India, and Europe regions. A more detailed description of the data is given in the CoVID19-FNIR_Documentation.pdf file.

Acknowledgment: This data collection and documentation was supported in part by the NSF: CO-WY AMP program, the Social Justice Research Center, and McNair Scholars Program, University of Wyoming, USA.

Please cite: Julio A. Saenz, Sindhu Reddy Kalathur Gopal, Diksha Shukla, June 12, 2021, "Covid-19 Fake News Infodemic Research Dataset (CoVID19-FNIR Dataset)", IEEE Dataport, doi: https://dx.doi.org/10.21227/b5bt-5244.

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3807 Views

Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans.

Instructions: 

 

“Dataset-S1” contains two folders for COVID-19 and Normal DICOM images, named as “COVID-S1” and “Normal-S1”, respectively. Within the same folder, three CSV files are available. The first one, named as “Radiologist-S1.csv”, contains labels assigned to the corresponding cases by three experienced radiologists. The second CSV file, “Clinical-S1.csv”, includes the clinical information as well as the result of the RT-PCR test, if available. The third file is named “LDCT-SL-Labels-S1.csv” and contains the slice-level labels related to COVID-19 cases. In other words, slices demonstrating infection are specified in this file.

Each row in this CSV file corresponds to a specific case, and each column represents the slice number in the volumetric CT scan. Label 1 indicates a slice with the evidence of infection, while 0 is assigned to slices with no evidence of infection.

Note that slices in each case should be sorted based on the “Slice-Location” value to match with the provided labels in the CSV file. The Slice Location values are stored in DICOM files and accessible from the following DICOM tag: (0020,1041) – DS – Slice Location

 “Dataset-S2” contains 100 COVID-19 positive cases, confirmed with RT-PCR test. 68 cases have related imaging findings, whereas 32 do not reveal signs of infection. These two groups are placed in two folders of “PCP-Lung-Positive “and “PCP-Lung-Negative”. “Dataset-S2” also includes a CSV file, namely “Clinical-S2.csv” presenting the clinical information.

 

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1321 Views

The dataset consists of two classes: COVID-19 cases and Healthy cases 

Instructions: 

Unzip the dataset

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1293 Views

We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 hours of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm).

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326 Views

This datasets contains Xrays of positive COVID-19 and Pneumonia patients.

For the COVID-19 class, three sources were used in this work, BIMCV-COVID-19+ (Spain), COVID-19- AR (USA) and V2-COV19-NII (Germany).

 

  

The pneumonia class data came from 3 sources: (i) the National Institute of Health (NIH) dataset, (ii) Chexpert dataset and (iii) Padchest dataset.

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736 Views

 Lung segmentation is essential in developing AI-assisted diagnosis methods. Here is the result of lung segmentation using morphological operation, and it has been used in our study. It contains 7053 CT slices in .jpg format. And the original dataset can be seen via  the Kaggle link https://www.kaggle.com/hgunraj/covidxct

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468 Views

The dataset collects the results of a survey of 325 respondents. Each respondent is asked to design a route from an origin to a destination taking into account the following considerations:

  • The route should avoid crowds to avoid getting COVID-19.
  • They should take into account the context provided: day, time, month, holiday period.

A total of 10 scenarios located in the city of Ciudad Real were designed.

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473 Views

The Ways To Wear a Mask or a Respirator Database (WWMR-DB) is a test database that can be used to compare the behavior of current mask detection systems with images that most closely resemble the real case. It consists of 1222 images divided into 8 classes, depicting the most common ways in which masks or respirators are worn:

- Mask Or Respirator Not Worn

- Mask Or Respirator Correctly Worn

- Mask Or Respirator Under The Nose

- Mask Or Respirator Under The Chin

- Mask Or Respirator Hanging From An Ear

- Mask Or Respirator On The Tip Of The Nose

Instructions: 

For any question, please send an email to antonio.marceddu@polito.it.

If you will use WWMR-DB dataset for your work, please cite the following paper:

A. C. Marceddu. R. Ferrero and B. Montrucchio, "Mask and respirator detection: analysis and potential solutions for a frequently ill-conditioned problem," 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 2022.

 

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2292 Views

This dataset has been developed based on the work of the GeoCOV19Tweets Dataset. The original work by Lamsal, R. runs network analysis on a similar dataset to understand the underlying relationship between countries and hashtags. The work did an analysis on roughly 300k number of [country, hashtag] relations from 190 countries and territories, and 5055 unique hashtags. This work pushes the number of relationships by 3 times.

Instructions: 

This dataset provides [place, hashtag] relationships in a Comma-separated values (CSV) file. Each line represents a relationship. You can simply use the CSV file as per your research needs.

However, if you need to change the place entity from city (currently the dataset uses ["place"]["name"] object) to country, you'll have to consider the ["place"]["country"] object instead. The sample script is provided with this dataset. The script takes in a list of tweet IDs present in a CSV file and hydrates the IDs to extract places and hashtags relationships. The script is written for twarc.

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3503 Views

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