  We establish a dataset of over $1.6\times10^4$ experimental images of
Bose--Einstein condensates containing solitonic excitations to enable machine
learning (ML) for many-body physics research. About $33~\%$ of this dataset has
manually assigned and carefully curated labels. The remainder is automatically
labeled using SolDet -- an implementation of a physics-informed ML data
analysis framework -- consisting of a convolutional-neural-network-based
classifier and OD as well as a statistically motivated physics-informed
classifier and a quality metric. This technical note constitutes the definitive
reference of the dataset, providing an opportunity for the data science
community to develop more sophisticated analysis tools, to further understand
nonlinear many-body physics, and even advance cold atom experiments.
