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Article type: Research Article
Authors: Meroño-Peñuela, Alberta; b; * | Ashkpour, Ashkanc | Guéret, Christophea; b | Schlobach, Stefana
Affiliations: [a] Department of Computer Science, VU University Amsterdam, De Boelelaan 1081a, 1081HV Amsterdam, The Netherlands. E-mails: albert.merono@vu.nl, c.d.m.gueret@vu.nl, k.s.schlobach@vu.nl | [b] Data Archiving and Networked Services, Anna van Saksenlaan 10, 2593HT Den Haag, The Netherlands. E-mails: albert.merono@dans.knaw.nl, christophe.gueret@dans.knaw.nl | [c] International Institute of Social History, Cruquiusweg 31, 1019AT Amsterdam, The Netherlands. E-mail: ashkan.ashkpour@iisg.nl
Correspondence: [*] Corresponding author. E-mail: albert.merono@vu.nl.
Abstract: Here, we describe the CEDAR dataset, a five-star Linked Open Data representation of the Dutch historical censuses. These were conducted in the Netherlands once every 10 years from 1795 to 1971. We produce a linked dataset from a digitized sample of 2,288 tables. It contains more than 6.8 million statistical observations about the demography, labour and housing of Dutch society in the 18th, 19th and 20th centuries. The dataset is modeled using the RDF Data Cube, Open Annotation, and PROV vocabularies. These are used to represent the multidimensionality of the data, to express rules of data harmonization, and to keep track of the provenance of all data points and their transformations, respectively. We link observations within the dataset to well known standard classification systems in social history, such as the Historical International Standard Classification of Occupations (HISCO) and the Amsterdamse Code (AC). The three contributions of the dataset are (1) an easier access to integrated census data for historical researchers; (2) richer connections to related Linked Data resources; and (3) novel concept schemes of historical relevance, like classifications of historical religions and historical house types.
Keywords: Social history, census data, Linked Open Data, RDF Data Cube
DOI: 10.3233/SW-160233
Journal: Semantic Web, vol. 8, no. 2, pp. 297-310, 2017
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