TIME MACHINE

TIME MACHINE : BIG DATA OF THE PAST FOR THE FUTURE OF EUROPE

Abstract

Europe urgently needs to restore and intensify its engagement with its past as a means of building a common path for the generations to come. Time Machine will give Europe the technology to renew itself against globalisation and increased social exclusion, by investing in its precious cultural heritage. The FET Flagship is structured around the development of a large-scale digitisation and computing infrastructure mapping millennia of European historical and geographical evolution, transforming kilometres of archives, large collections from museums and other geohistorical datasets into a distributed digital information system. To succeed, a series of fundamental breakthroughs are targeted in Artificial Intelligence, Robotics and ICT, boosting these key enabling technologies in Europe. Massive digitisation infrastructures and High-Performance Computing will be coupled with Machine Learning techniques to produce a multiscale simulation of more than 5000 years of history. Time Machine will make Europe the leader in the extraction and analysis of Big Data of the Past. It will profoundly transform research methods and practices in the Humanities, allowing bolder questions to be asked and new levels of understanding to be reached. It will bring a new era of open access to sources, where past and on-going research are open science. This constant source of new knowledge will be an economic motor, giving rise to new professions, new services and new products, impacting not only on education, cultural heritage and creative industries, but also policy making, and economic societal and environmental modelling. Time Machine is based on Europe's unique assets: its long history, its multilingualism and multiculturalism. It is designed to bring together European research institutions, cultural heritage stakeholders, decision makers, businesses and the general public in a unique endeavour: turning the history of Europe into a living resource for co-creating its future.

Project details

Unibo Team Leader: Fabio Vitali

Unibo involved Department/s:
Dipartimento di Informatica - Scienza e Ingegneria

Coordinator:
Epfl Ecole Politechnique Federale De Lausanne(Switzerland)

Other Participants:
Stichting Europeana (Netherlands)
Friedrich Alexander Universitaet Erlangen Nuernberg (Germany)
Westerdijk Fungal Biodiversity Institute (Netherlands)
Universiteit Van Amsterdam (Netherlands)
Uniwersytet Warszawski - University Of Warsaw (Poland)
Institut National De L'Information Geographique Et Forestiere (France)
Ecole Nationale Des Chartes (France)
Fraunhofer Ipa (Germany)
Università  Cà  Foscari di VENEZIA (Italy)
ALMA MATER STUDIORUM - Università di Bologna (Italy)
Stichting Nederlands Instituut Voorbeeld En Geluid (Netherlands)
INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ AKADEMII NAUK-Institute of Bioorganic Chemistry Polish Academy of Sciences (Poland)
Indra Sistemas S.A. (Spain)
Technische Universität Wien (Austria)
Universiteit Antwerpen (Belgium)
Ubisoft Entertainment Sa (France)
Iconem (France)
Bar Ilan University - Research Authority (Israel)
Centre De Visio Per Computador (Spain)
Universite Du Luxembourg (Luxembourg)
International Centre for Archival Research (ICARUS) (Austria)
Naver France (France)
Qidenus Group Gmbh (Germany)
Universiteit Utrecht (Netherlands)
Technische Universiteit Delft - Delft University Of Technology (Netherlands)
Picturae Bv (Netherlands)
Technische Universitaet Dortmund (Germany)
Fiz Karlsruhe (Germany)
Cnrs (France)
Technische Universitat Dresden (Germany)
Osterreichische Nationalbibliothek (Austria)
Universiteit Gent (Belgium)

Total Eu Contribution: Euro (EUR) 997.930,00
Project Duration in months: 12
Start Date: 01/03/2019
End Date: 28/02/2020

Cordis webpage
Project website

Sustainable cities This project contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820323 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820323