  How do we know how much we know? Quantifying uncertainty associated with our
modelling work is the only way we can answer how much we know about any
phenomenon. With quantitative science now highly influential in the public
sphere and the results from models translating into action, we must support our
conclusions with sufficient rigour to produce useful, reproducible results.
Incomplete consideration of model-based uncertainties can lead to false
conclusions with real world impacts. Despite these potentially damaging
consequences, uncertainty consideration is incomplete both within and across
scientific fields. We take a unique interdisciplinary approach and conduct a
systematic audit of model-related uncertainty quantification from seven
scientific fields, spanning the biological, physical, and social sciences. Our
results show no single field is achieving complete consideration of model
uncertainties, but together we can fill the gaps. We propose opportunities to
improve the quantification of uncertainty through use of a source framework for
uncertainty consideration, model type specific guidelines, improved
presentation, and shared best practice. We also identify shared outstanding
challenges (uncertainty in input data, balancing trade-offs, error propagation,
and defining how much uncertainty is required). Finally, we make nine concrete
recommendations for current practice (following good practice guidelines and an
uncertainty checklist, presenting uncertainty numerically, and propagating
model-related uncertainty into conclusions), future research priorities
(uncertainty in input data, quantifying uncertainty in complex models, and the
importance of missing uncertainty in different contexts), and general research
standards across the sciences (transparency about study limitations and
dedicated uncertainty sections of manuscripts).
