Digitisation has changed archaeology deeply and has increased exponentially the amount of data that could be processed, but it does not by itself involve datafication, which is the act of transforming something (objects, processes, etc.) into a quantified format, so they can be tabulated and analysed. Datafication fits a Big Data approach and promises to go significantly beyond digitisation. To datafy archaeology would mean to produce a flow of data starting from the data produced by the archaeological practice, for instance, locations, interactions and relations between finds and sites. The ArchAIDE project goes exactly in this direction. ArchAIDE is a H2020 funded project (2016-2019) that will realise a tool for recognising archaeological potsherds; a web-based real-time data visualization to generate new understanding; an open archive to allow the archival and re-use of ar-chaeological data. This process would move archaeology towards data-driven research and Big Data.

Big Archaeological Data. The ArchAIDE project approach

ANICHINI F.
Co-primo
;
GATTIGLIA G
Co-primo
2018-01-01

Abstract

Digitisation has changed archaeology deeply and has increased exponentially the amount of data that could be processed, but it does not by itself involve datafication, which is the act of transforming something (objects, processes, etc.) into a quantified format, so they can be tabulated and analysed. Datafication fits a Big Data approach and promises to go significantly beyond digitisation. To datafy archaeology would mean to produce a flow of data starting from the data produced by the archaeological practice, for instance, locations, interactions and relations between finds and sites. The ArchAIDE project goes exactly in this direction. ArchAIDE is a H2020 funded project (2016-2019) that will realise a tool for recognising archaeological potsherds; a web-based real-time data visualization to generate new understanding; an open archive to allow the archival and re-use of ar-chaeological data. This process would move archaeology towards data-driven research and Big Data.
2018
9788890507779
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/923365
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