FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities
Pavlos Fafalios, Kostas Petrakis, Georgios Samaritakis, Korina Doerr,, Athina Kritsotaki, Yannis Tzitzikas, Martin Doerr

TL;DR
FAST CAT is a collaborative system designed to improve data entry and curation in Digital Humanities, addressing limitations of traditional tools by supporting semantic interoperability and enhancing data reuse and verification.
Contribution
The paper introduces FAST CAT, a novel collaborative platform that enhances data entry, curation, and semantic interoperability in empirical research within Digital Humanities.
Findings
Implemented in a European Maritime History project
Improved data consistency and interoperability
Facilitated revisiting and verifying original data sources
Abstract
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…
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