  Modern science, technology, and politics are all permeated by data that comes
from people, measurements, or computational processes. While this data is often
incomplete, corrupt, or lacking in sufficient accuracy and precision, explicit
consideration of uncertainty is rarely part of the computational and decision
making pipeline. The CCC Workshop on Quantification, Communication, and
Interpretation of Uncertainty in Simulation and Data Science explored this
problem, identifying significant shortcomings in the ways we currently process,
present, and interpret uncertain data. Specific recommendations on a research
agenda for the future were made in four areas: uncertainty quantification in
large-scale computational simulations, uncertainty quantification in data
science, software support for uncertainty computation, and better integration
of uncertainty quantification and communication to stakeholders.
