Machine learning tools have empowered a qualitatively new way to perform
differential cross section measurements whereby the data are unbinned, possibly in many
dimensions. Unbinned measurements can enable, improve, or at least simplify comparisons
between experiments and with theoretical predictions. Furthermore, many-dimensional
measurements can be used to define observables after the measurement instead of before.
There is currently no community standard for publishing unbinned data. While there are
also essentially no measurements of this type public, unbinned measurements are expected
in the near future given recent methodological advances. The purpose is to
propose a scheme for presenting and using unbinned results, which can hopefully form the
basis for a community standard to allow for integration into analysis workflows. This is
foreseen to be the start of an evolving community dialogue, in order to accommodate future
developments in this field that is rapidly evolving.