  Pollution of online social spaces caused by rampaging d/misinformation is a
growing societal concern. However, recent decisions to reduce access to social
media APIs are causing a shortage of publicly available, recent, social media
data, thus hindering the advancement of computational social science as a
whole. We present a large, high-coverage dataset of social interactions and
user-generated content from Bluesky Social to address this pressing issue. The
dataset contains the complete post history of over 4M users (81% of all
registered accounts), totalling 235M posts. We also make available social data
covering follow, comment, repost, and quote interactions. Since Bluesky allows
users to create and bookmark feed generators (i.e., content recommendation
algorithms), we also release the full output of several popular algorithms
available on the platform, along with their timestamped ``like'' interactions
and time of bookmarking. This dataset allows unprecedented analysis of online
behavior and human-machine engagement patterns. Notably, it provides
ground-truth data for studying the effects of content exposure and
self-selection and performing content virality and diffusion analysis.
