  Descriptive and empirical sciences, such as History, are the sciences that
collect, observe and describe phenomena in order to explain them and draw
interpretative conclusions about influences, driving forces and impacts under
given circumstances. Spreadsheet software and relational database management
systems are still the dominant tools for quantitative analysis and overall data
management in these these sciences, allowing researchers to directly analyse
the gathered data and perform scholarly interpretation. However, this current
practice has a set of limitations, including the high dependency of the
collected data on the initial research hypothesis, usually useless for other
research, the lack of representation of the details from which the registered
relations are inferred, and the difficulty to revisit the original data sources
for verification, corrections or improvements. To cope with these problems, in
this paper we present FAST CAT, a collaborative system for assistive data entry
and curation in Digital Humanities and similar forms of empirical research. We
describe the related challenges, the overall methodology we follow for
supporting semantic interoperability, and discuss the use of FAST CAT in the
context of a European (ERC) project of Maritime History, called SeaLiT, which
examines economic, social and demographic impacts of the introduction of
steamboats in the Mediterranean area between the 1850s and the 1920s.
