Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1677
Title: Integrated statistical indicators from Scottish linked open government data
Authors: Karamanou, Areti
Kalampokis, Evangelos
Tarabanis, Konstantinos
Type: Article
Subjects: FRASCATI::Engineering and technology
Keywords: Integrated statistical indicators
Linked data
Linked data
Open government data
Scottish statistics
Machine learning
Issue Date: 2023
Source: Data in Brief
Volume: 46
First Page: 108779
Abstract: Open 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.
URI: https://doi.org/10.1016/j.dib.2022.108779
https://ruomo.lib.uom.gr/handle/7000/1677
ISSN: 2352-3409
Other Identifiers: 10.1016/j.dib.2022.108779
Appears in Collections:Department of Business Administration

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