Understanding data uncertainty

Scientific data without uncertainty estimates are increasingly seen as incomplete. Recent discussions in the philosophy of data, however, have given little attention to the nature of uncertainty estimation. We begin to redress this gap by, first, discussing the concepts and practices of uncertainty estimation in metrology and showing how they can be adapted for scientific data more broadly; and second, advancing five philosophical theses about uncertainty estimates for data: they are substantive epistemic products; they are fallible; they can be iteratively improved; they should be judged in terms of their adequacy-for-purpose; and these estimates, in turn, are essential for judging data adequacy. We illustrate these five theses using the example of the GISTEMP global temperature dataset. Our discussion introduces a novel adequacy-for-purpose view of uncertainty estimation, addresses a weakness in a recent philosophical account of data, and provides a new perspective on the "safety" versus "precision" debate in metrology.