  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 of this paper 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.
