Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο:
https://ruomo.lib.uom.gr/handle/7000/1555
Τίτλος: | Integrated statistical indicators from Scottish linked open government data |
Συγγραφείς: | Karamanou, Areti Kalampokis, Evangelos Tarabanis, Konstantinos |
Τύπος: | Article |
Θέματα: | FRASCATI::Natural sciences::Computer and information sciences |
Λέξεις-Κλειδιά: | Integrated statistical indicators Linked data Machine learning Open government data Scottish statistics |
Ημερομηνία Έκδοσης: | Φεβ-2023 |
Πηγή: | Data in brief |
Τόμος: | 46 |
Πρώτη Σελίδα: | 108779 |
Επιτομή: | 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/7000/1555 |
ISSN: | 2352-3409 |
Ηλεκτρονικό ISSN: | 2352-3409 |
Αλλοι Προσδιοριστές: | 10.1016/j.dib.2022.108779 |
Εμφανίζεται στις Συλλογές: | Τμήμα Οργάνωσης & Διοίκησης Επιχειρήσεων |
Αρχεία σε αυτό το Τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
---|---|---|---|---|
1-s2.0-S2352340922009829-main-2.pdf | 1,13 MB | Adobe PDF | Προβολή/Ανοιγμα |
Αυτό το τεκμήριο προστατεύεται από Αδεια Creative Commons