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dc.contributor.authorKaramanou, Areti-
dc.contributor.authorKalampokis, Evangelos-
dc.contributor.authorTarabanis, Konstantinos-
dc.date.accessioned2023-11-03T07:22:23Z-
dc.date.available2023-11-03T07:22:23Z-
dc.date.issued2023-
dc.identifier10.1016/j.dib.2022.108779en_US
dc.identifier.issn2352-3409en_US
dc.identifier.urihttps://doi.org/10.1016/j.dib.2022.108779en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/1677-
dc.description.abstractOpen Government Data (OGD), including statistical data, such as economic, environmental and social indicators, are data published by the public sector for free reuse. These data have a huge potential when exploited using Machine Learning methods. Linked Data technologies facilitate retrieving integrated statistical indicators by defining and executing SPARQL queries. However, statistical indicators are available in different temporal and spatial granularity levels as well using different units of measurement. This data article describes the integrated statistical indicators that were retrieved from the official Scottish data portal in order to facilitate the exploitation of Machine Learning methods in OGD. Multiple SPARQL queries as well as manual search in the data portal were employed towards this end. The resulted dataset comprises the maximum number of compatible datasets, i.e., datasets with matching temporal and spatial characteristics. In particular, the data include 60 statistical indicators from seven categories such as health and social care, housing, and crime and justice. The indicators refer to the 6,976 “2011 data zones” of Scotland, while the year of reference is 2015. Data are ready to be used by the research community, students, policy makers, and journalists and give rise to plenty of social, business, and research scenarios that can be solved using Machine Learning technologies and methods.en_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceData in Briefen_US
dc.subjectFRASCATI::Engineering and technologyen_US
dc.subject.otherIntegrated statistical indicatorsen_US
dc.subject.otherLinked dataen_US
dc.subject.otherLinked dataen_US
dc.subject.otherOpen government dataen_US
dc.subject.otherScottish statisticsen_US
dc.subject.otherMachine learningen_US
dc.titleIntegrated statistical indicators from Scottish linked open government dataen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Οργάνωσης & Διοίκησης Επιχειρήσεωνen_US
local.identifier.volume46en_US
local.identifier.firstpage108779en_US
Εμφανίζεται στις Συλλογές: Τμήμα Οργάνωσης & Διοίκησης Επιχειρήσεων

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