  HAL (Hyper Articles en Ligne) is the French national publication repository,
used by most higher education and research organizations for their open science
policy. As a digital library, it is a rich repository of scholarly documents,
but its potential for advanced research has been underutilized. We present
HALvest, a unique dataset that bridges the gap between citation networks and
the full text of papers submitted on HAL. We craft our dataset by filtering HAL
for scholarly publications, resulting in approximately 700,000 documents,
spanning 34 languages across 13 identified domains, suitable for language model
training, and yielding approximately 16.5 billion tokens (with 8 billion in
French and 7 billion in English, the most represented languages). We transform
the metadata of each paper into a citation network, producing a directed
heterogeneous graph. This graph includes uniquely identified authors on HAL, as
well as all open submitted papers, and their citations. We provide a baseline
for authorship attribution using the dataset, implement a range of
state-of-the-art models in graph representation learning for link prediction,
and discuss the usefulness of our generated knowledge graph structure.
