  This work introduces TopoBenchmarkX, a modular open-source library designed
to standardize benchmarking and accelerate research in Topological Deep
Learning (TDL). TopoBenchmarkX maps the TDL pipeline into a sequence of
independent and modular components for data loading and processing, as well as
model training, optimization, and evaluation. This modular organization
provides flexibility for modifications and facilitates the adaptation and
optimization of various TDL pipelines. A key feature of TopoBenchmarkX is that
it allows for the transformation and lifting between topological domains. This
enables, for example, to obtain richer data representations and more
fine-grained analyses by mapping the topology and features of a graph to
higher-order topological domains such as simplicial and cell complexes. The
range of applicability of TopoBenchmarkX is demonstrated by benchmarking
several TDL architectures for various tasks and datasets.
