  Quality data is a fundamental contributor to success in statistics and
machine learning. If a statistical assessment or machine learning leads to
decisions that create value, data contributors may want a share of that value.
This paper presents methods to assess the value of individual data samples, and
of sets of samples, to apportion value among different data contributors. We
use Shapley values for individual samples and Owen values for combined samples,
and show that these values can be computed in polynomial time in spite of their
definitions having numbers of terms that are exponential in the number of
samples.
