Identification (biology)

Physics

World Wide Web

Pathology

Psychology

Social distance

Pedagogy

Alternative medicine

Geography

Data science

Tracking (education)

Misinformation

Scale (ratio)

Disease

Event (particle physics)

Data sharing

Open data

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Coronavirus disease 2019 (COVID-19)

Computer security

Computer science

Social media

Infectious disease (medical specialty)

Cartography

Botany

Quantum mechanics

Medicine

Biology

Pandemic

Epidemiologia

As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration