COVID-19

The dataset contains 877 records with 67 variables, documenting various COVID-19-related indicators in Indonesia. The data spans daily case statistics, including new cases, cumulative cases, recoveries, and fatalities. It also tracks government response measures, such as school and workplace closures, public event restrictions, travel controls, and mask mandates. Additionally, it includes vaccination statistics, government response indices, and mobility changes in different sectors (retail, workplaces, and residential areas).

The dataset contains 877 records with 67 variables, documenting various COVID-19-related indicators in Indonesia. The data spans daily case statistics, including new cases, cumulative cases, recoveries, and fatalities. It also tracks government response measures, such as school and workplace closures, public event restrictions, travel controls, and mask mandates. Additionally, it includes vaccination statistics, government response indices, and mobility changes in different sectors (retail, workplaces, and residential areas).

The COVID-19 Vaccine Misinformation Aspects Dataset contains 3,822 English tweets discussing COVID-19 vaccine misinformation, collected from Twitter/X between December 31, 2020, and July 8, 2021. Each tweet is manually annotated and categorized into four distinct misinformation aspects: (1) Vaccine Constituent, (2) Adverse Effects, (3) Agenda-Driven Narratives, and (4) Efficacy and Clinical Trials.

The TripAdvisor online airline review dataset, spanning from 2016 to 2023, provides a comprehensive collection of passenger feedback on airline services during the COVID-19 pandemic. This dataset includes user-generated reviews that capture sentiments, preferences, and concerns, allowing for an in-depth analysis of shifting customer priorities in response to pandemic-related disruptions. By examining these reviews, the dataset facilitates the study of evolving passenger expectations, changes in service perceptions, and the airline industry's adaptive strategies.

This dataset includes long-term symptom prevalence data for 27 physical and mental health symptoms associated with Long COVID, extracted from 136 studies spanning up to three years. Key symptoms include fatigue, joint pain, myalgia, respiratory issues (dyspnea, cough), sensory impairments (anosmia, ageusia), neurological symptoms (brain fog), and mental health challenges (anxiety, depression, insomnia).

Please cite the following paper when using this dataset:

Vanessa Su and Nirmalya Thakur, “COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations”, Proceedings of the IEEE 15th Annual Computing and Communication Workshop and Conference 2025, Las Vegas, USA, Jan 06-08, 2025 (Paper accepted for publication, Preprint: https://arxiv.org/abs/2412.17180).

Abstract:

To download this dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13896353

Please cite the following paper when using this dataset:

To download the dataset without purchasing an IEEE Dataport subscription, please visit: https://zenodo.org/records/13738598

Please cite the following paper when using this dataset:

N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292

Abstract

Persistent viruses like influenza, HIV, Coronavirus exemplify the challenge of viral escape, significantly hindering the development of long-lasting vaccines and effective treatments.  This study leverages a Long Short-Term Memory (LSTM) based deep learning architecture to analyze an extensive dataset of over 3.1 million unique viral spike protein sequences, with SARS-CoV-2 serving as the primary example. Our model, Escape Elite Network(EEN) outperforms existing methods in detecting escape mutations across diverse datasets.

This dataset named "Chest X-ray images for Multiple diseases" is a medium sized dataset we collected and produced in 2024 from various sources to predict various Chest-X-ray diseases using Deep learning techniques, primarily from Radiopaedia.org, coronacases.org, Kaggle contains 1000 images for each of the disease namely TB,pneumonia,Covid-19,Normal. This dataset is designed to support the evaluation and development of algorithms to predict various chest x-ray diseases.