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dc.contributor.authorMartínez-Durive, Orlando E. 
dc.contributor.authorMishra, Sachit 
dc.contributor.authorZiemlicki, Cezary
dc.contributor.authorRubrichi, Stefania
dc.contributor.authorSmoreda, Zbigniew
dc.contributor.authorFiore, Marco 
dc.date.accessioned2024-01-12T10:28:40Z
dc.date.available2024-01-12T10:28:40Z
dc.date.issued2023-05-11
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1778
dc.description.abstractDigital sources have been enabling unprecedented data-driven and large-scale investigations across a wide range of domains, including demography, sociology, geography, urbanism, criminology, and engineering. A major barrier to innovation is represented by the limited availability of dependable digital datasets, especially in the context of data gathered by mobile network operators or service providers, due to concerns about user privacy and industrial competition. The resulting lack of reference datasets curbs the production of new research methods and results, and prevents verifiability and reproducibility of research outcomes. The NetMob23 dataset offers a rare opportunity to the multidisciplinary research community to access rich data about the spatio-temporal consumption of mobile applications in a developed country. The generation process of the dataset sets a new quality standard, leading to information about the demands generated by 68 popular mobile services, geo-referenced at a high resolution of 100×100 m2 over 20 metropolitan areas in France, and monitored during 77 consecutive days in 2019es
dc.description.sponsorshipComunidad de Madrides
dc.description.sponsorshipFrench National Research Agency (ANR)es
dc.language.isoenges
dc.publisherArxives
dc.titleThe NetMob23 Dataset: A High-resolution Multi-region Service-level Mobile Data Traffic Cartographyes
dc.typetechnical reportes
dc.type.hasVersionVoRes
dc.rights.accessRightsopen accesses
dc.monograph.typetechnical_reportes
dc.relation.projectNameAttraction of research talent. NetSensees
dc.relation.projectNameCoCo5G (Traffic Collection, Contextual Analysis. Data-driven Optimization for 5G)es
dc.identifier.reportTR-IMDEA-Networks-2023-3es


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