  In the context of journalism, the COVID-19 pandemic brought unprecedented
challenges, necessitating rapid adaptations in newsrooms. Data journalism
emerged as a pivotal approach for effectively conveying complex information to
the public. Here, we show the profound impact of COVID-19 on data journalism,
revealing a surge in data-driven publications and heightened collaboration
between data and science journalists. Employing a quantitative methodology,
including negative binomial regression and Relational hyperevent models (RHEM),
on byline data of articles co-authored by data journalists, we comprehensively
analyze data journalism outputs, authorship trends, and collaboration networks
to address five key research questions.
  The findings reveal a significant increase in data journalistic pieces during
and after the pandemic, in particular with a rise in publications within
scientific departments. Collaborative efforts among data and science
journalists intensified, evident through increased authorship and co-authorship
trends. Prior common authorship experiences somewhat influenced the likelihood
of future co-authorships, underscoring the importance of building collaborative
communities of practice.
  These quantitative insights provide an understanding of the transformational
role of data journalism during COVID-19, contributing to the growing body of
literature in computational communication science and journalism practice.
