Alessandro Codello’s contribution introduces “Long Data” as a novel approach to unlocking the cultural heritage within historical archives. This concept contrasts with Big Data by focusing on the deep historical context found in meticulously preserved archives, revealing insights into cultural heritage. Utilizing new Artificial Intelligence technologies in harmony with traditional archival methods, Long Data aims to analyze, transcribe, and model historical data on an unprecedented scale. This approach promises a more comprehensive understanding of history, enhancing studies on societal and cultural evolution. A key example of Long Data’s application is the Venice State Archive (ASVe), which holds over a millennium’s worth of documents. The initiative seeks multidisciplinary collaboration to make accessible this vast archive, thereby safeguarding its cultural heritage and preparing the ground for a revolution in historical research.

The New Science of Long Data

Codello, Alessandro
2023-01-01

Abstract

Alessandro Codello’s contribution introduces “Long Data” as a novel approach to unlocking the cultural heritage within historical archives. This concept contrasts with Big Data by focusing on the deep historical context found in meticulously preserved archives, revealing insights into cultural heritage. Utilizing new Artificial Intelligence technologies in harmony with traditional archival methods, Long Data aims to analyze, transcribe, and model historical data on an unprecedented scale. This approach promises a more comprehensive understanding of history, enhancing studies on societal and cultural evolution. A key example of Long Data’s application is the Venice State Archive (ASVe), which holds over a millennium’s worth of documents. The initiative seeks multidisciplinary collaboration to make accessible this vast archive, thereby safeguarding its cultural heritage and preparing the ground for a revolution in historical research.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5099007
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