The Data Atlas is the centerpiece of the PERISCOPE project’s data-driven research. The Atlas constitutes a centralized access point for the exploration, visualization and analysis of the original data produced by PERISCOPE partners, integrated with the most relevant information about the COVID-19 pandemic and its effects on health, economics, policy-making, and society at large. The Atlas interfaces and tools make such data readily available to the research community, decision makers and the general public, providing the means to amplify its reach and impact. The present demo, showcases the features of v1.2 release of the Atlas, 18 months from the project kick-off, and some of the planned enhancements to be delivered until project month 24.

The PERISCOPE Data Atlas: A Demonstration of Release v1.2

Parimbelli E.;Larizza C.;Ottaviano M.;Pala D.;Casella V.;Bellazzi R.;Giudici P.
2022-01-01

Abstract

The Data Atlas is the centerpiece of the PERISCOPE project’s data-driven research. The Atlas constitutes a centralized access point for the exploration, visualization and analysis of the original data produced by PERISCOPE partners, integrated with the most relevant information about the COVID-19 pandemic and its effects on health, economics, policy-making, and society at large. The Atlas interfaces and tools make such data readily available to the research community, decision makers and the general public, providing the means to amplify its reach and impact. The present demo, showcases the features of v1.2 release of the Atlas, 18 months from the project kick-off, and some of the planned enhancements to be delivered until project month 24.
2022
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Esperti anonimi
Inglese
contributo
20th International Conference on Artificial Intelligence in Medicine, AIME 2022
2022
Halifax, Canada
Internazionale
ELETTRONICO
Lecture notes in artificial intelligence
13263
412
415
4
978-3-031-09341-8
978-3-031-09342-5
Springer Science and Business Media Deutschland GmbH
COVID-19; Data integration; Data warehouse; GIS; Impact; Ontology; Pandemic; Policy
https://link.springer.com/chapter/10.1007/978-3-031-09342-5_41
none
Parimbelli, E.; Larizza, C.; Urosevic, V.; Pogliaghi, A.; Ottaviano, M.; Cheng, C.; Benoit, V.; Pala, D.; Casella, V.; Bellazzi, R.; Giudici, P....espandi
273
info:eu-repo/semantics/conferenceObject
11
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1462045
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