  Friendship and rapport play an important role in the formation of
constructive social interactions, and have been widely studied in educational
settings due to their impact on student outcomes. Given the growing interest in
automating the analysis of such phenomena through Machine Learning (ML), access
to annotated interaction datasets is highly valuable. However, no dataset on
dyadic child-child interactions explicitly capturing rapport currently exists.
Moreover, despite advances in the automatic analysis of human behaviour, no
previous work has addressed the prediction of rapport in child-child dyadic
interactions in educational settings. We present UpStory -- the Uppsala
Storytelling dataset: a novel dataset of naturalistic dyadic interactions
between primary school aged children, with an experimental manipulation of
rapport. Pairs of children aged 8-10 participate in a task-oriented activity:
designing a story together, while being allowed free movement within the play
area. We promote balanced collection of different levels of rapport by using a
within-subjects design: self-reported friendships are used to pair each child
twice, either minimizing or maximizing pair separation in the friendship
network. The dataset contains data for 35 pairs, totalling 3h 40m of audio and
video recordings. It includes two video sources covering the play area, as well
as separate voice recordings for each child. An anonymized version of the
dataset is made publicly available, containing per-frame head pose, body pose,
and face features; as well as per-pair information, including the level of
rapport. Finally, we provide ML baselines for the prediction of rapport.
