This dataset includes the daily reported cases and deaths data of COVID-19 pandemic in pakistan and a public whatsapp group conversation on the helath-related issues during pandemic. all the data is anonymized and do not show the identity of any person.

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A dataset contains a total of 578,375 COVID-19 confirmed cases reported in Thailand that were being recorded between 22 January 2021 to 30 July 2021.

Daily reports of the COVID-19 situation in Thailand can be download from https://data.go.th/en/dataset/covid-19-daily.

Resource ID: 87abdf57-7edd-4864-9766-7bb0e87272f9

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99 Views

BIMCV-COVID19- dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of no COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).

Instructions: 

Once all the compressed files have been downloaded, use 00_extract_data.sh for their correct decompression. For more information, you could see the links on this page.

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275 Views

BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).

Instructions: 

Once all the compressed files have been downloaded, use 00_extract_data.sh for their correct decompression. For more information, you could see the links on this page

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462 Views

This data resource is an outcome of the NSF RAPID project titled "Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab" awarded to University of Missouri-Columbia.

The resource contains the output of variant analysis (along with CADD scores) on human genome sequences obtained from the COVID-19 Data Portal. The variants include single nucleotide polymorphisms (SNPs) and short insert and deletes (indels).

Instructions: 

1. Download a .zip file.

2. Unzip the file and extract it into a folder. 

3. There will be two folders, namely, VCF and CADD_Scores. These folders contain the compressed .vcf and .tsv files. The .vcf files are filtered VCF files produced by the GATK best practice workflow for RNA-seq data. The reference genome hg19 was used. There is also a .xlsx file containing the run accession IDs (e.g., SRR12095153) and URLs (e.g., https://www.ebi.ac.uk/ena/browser/view/SRR12095153) from where the paired end sequences were downloaded. Complete description of the sequences can be found via these URLs.

4. Check for new .zip files.

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710 Views

The dataset contains the data on ICU-transferred (N=100) and Stable (N=131) patients with COVID-19 (N=156) and Non-COVID-19 viral pneumonia (N=75). Among COVID-19 patients of this study, 82 patients developed Refractory Respiratory Failure (RRF) or Severe Acute Respiratory Distress Syndrome (SARDS) and were transferred to Intensive Care Unit (ICU), 74 patients had a Stable course of disease and were not transferred to ICU.

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501 Views

Leaderboard (numbers are kW MAE):

Teams with more than 5 missing submissions are eliminated from the leaderboard.

 

Last Updated On: 
Mon, 05/10/2021 - 20:31

The dataset links to the survey performed on students and professors of Biological Engineering introductory course, as the Department of Biological Engineering, University of the Republic, Uruguay.

Instructions: 

The dataset is meant for pure academic and non-commerical use.

For queries, please consult the corresponding author (Parag Chatterjee, paragc@ieee.org).

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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.

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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2871 Views

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