  Many aspects of macroevolutionary theory and our understanding of biotic
responses to global environmental change derive from literature-based
compilations of palaeontological data. Existing manually assembled databases
are, however, incomplete and difficult to assess and enhance. Here, we develop
and validate the quality of a machine reading system, PaleoDeepDive, that
automatically locates and extracts data from heterogeneous text, tables, and
figures in publications. PaleoDeepDive performs comparably to humans in complex
data extraction and inference tasks and generates congruent synthetic
macroevolutionary results. Unlike traditional databases, PaleoDeepDive produces
a probabilistic database that systematically improves as information is added.
We also show that the system can readily accommodate sophisticated data types,
such as morphological data in biological illustrations and associated textual
descriptions. Our machine reading approach to scientific data integration and
synthesis brings within reach many questions that are currently underdetermined
and does so in ways that may stimulate entirely new modes of inquiry.
