  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.
