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CORDIS - EU research results
CORDIS

Secure Interactive Environments for SensiTive data Analytics

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

1st SIESTA platform release (opens in new window)

This is the 1st SIESTA platform release, in the form of an online platform delivered to users.

Report on demography applications (opens in new window)

Report about use case implementation and status, collected user feedback and suggestions for improvements.

Report on the medical imaging applications (opens in new window)

Report about use case implementation and status, collected user feedback and suggestions for improvements.

Policy Briefing (opens in new window)

Policy briefing deliverable for the SIESTA project.

Report on the tension between FAIR guiding principles and sensitive data (opens in new window)

A detailed analysis on the tension between each of the 15 FAIR principles and their limitations due to the content or the rights over the data

Report on the SIESTA epidemiology tool (opens in new window)

Report about use case implementation and status, collected user feedback and suggestions for improvements.

Report on text anonymization tools (opens in new window)

Report about use case implementation and status, collected user feedback and suggestions for improvements.

Requirements and architecture definition (opens in new window)

This document provides the first architecture specification of the SIESTA platform, together with the requirements elicitation activities carried out with the use cases.

SIESTA anonymisation tools (opens in new window)

Software release containing a set of anonymization tools implemented within the project.

Publications

How floods may affect the spatial spread of respiratory pathogens: the case of Emilia-Romagna, Italy in May 2023 (opens in new window)

Author(s): Claudio Ascione; Eugenio Valdano
Published in: EPJ Data Science, 2024, ISSN 2193-1127
Publisher: Springer
DOI: 10.1101/2024.09.20.24314056

Collaborative forecasting of influenza-like illness in Italy: the Influcast experience (opens in new window)

Author(s): Stefania Fiandrino; Andrea Bizzotto; Giorgio Guzzetta; Stefano Merler; Federico Baldo; Eugenio Valdano; Alberto Mateo Urdiales; Antonino Bella; Francesco Celino; Lorenzo Zino; Alessandro Rizzo; Yuhan Li; Nicola Perra; Corrado Gioannini; Paolo Milano; Daniela Paolotti; Marco Quaggiotto; Luca Rossi; Ivan Vismara; Alessandro Vespignani; Nicolò Gozzi
Published in: Epidemics, 2024, ISSN 1878-0067
Publisher: Elsevier
DOI: 10.1016/J.EPIDEM.2025.100819

Resilience of mobility network to dynamic population response across COVID-19 interventions: Evidences from Chile (opens in new window)

Author(s): Pasquale Casaburi; Lorenzo Dall’Amico; Nicolò Gozzi; Kyriaki Kalimeri; Anna Sapienza; Rossano Schifanella; T. Di Matteo; Leo Ferres; Mattia Mazzoli
Published in: PLOS Computational Biology, 2025, ISSN 1553-7358
Publisher: Public Library of Science (PLoS)
DOI: 10.48550/ARXIV.2405.19141

An Open Source Python Library for Anonymizing Sensitive Data (opens in new window)

Author(s): Sáinz-Pardo Díaz, Judith; López García, Álvaro
Published in: Scientific Data, 2024, ISSN 2052-4463
Publisher: Springer Nature
DOI: 10.1038/S41597-024-04019-Z

BMC Public Health (opens in new window)

Author(s): Kathleen Kelley; Nicolò Gozzi; Mattia Mazzoli; Daniela Paolotti
Published in: BMC Public Health, 2025, ISSN 1471-2458
Publisher: Springer Nature
DOI: 10.1186/S12889-025-22496-8

A generalized vector-field framework for mobility (opens in new window)

Author(s): Erjian Liu, Mattia Mazzoli, Xiao-Yong Yan, José J. Ramasco
Published in: Communications Physics, Issue 7, 2024, ISSN 2399-3650
Publisher: Springer Science and Business Media LLC
DOI: 10.1038/S42005-024-01672-Z

Author Correction: An Open Source Python Library for Anonymizing Sensitive Data (opens in new window)

Author(s): Judith Sáinz-Pardo Díaz; Álvaro López García
Published in: Scientific Data, 2024, ISSN 2052-4463
Publisher: Springer Nature
DOI: 10.1038/S41597-024-04283-Z

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