Familiar Wikidata: The Case for Building a Data Source We Can Trust
Wikipedia is far from perfect. The same can be said of its sister project, Wikidata. And yet, excluding the World Wide Web itself, Wikipedia and Wikidata together represent the world’s largest structured humanities data source. This methods…
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Version 1.0 - published on 13 Jun 2022 doi: 10.25547/FV61-JD37 - cite this
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Wikipedia is far from perfect. The same can be said of its sister project, Wikidata. And yet, excluding the World Wide Web itself, Wikipedia and Wikidata together represent the world’s largest structured humanities data source. This methods paper offers an introduction to the value of Wikidata for humanities research and makes the case for humanities researchers’ intervention in its development. It concludes with a short case study to illustrate how Wikidata can support humanities research projects. The case study project, Linked Familiarity, uses Wikidata data about the people quoted in the first ten editions of Bartlett’s Familiar Quotations to look for patterns in the people Bartlett’s Familiar editorial team thought readers find quotable from 1855 and 1910. These patterns will, we hope, clarify a corner of the zeitgeist: Bartlett’s Familiar Quotations readers voted with their purchases—the book’s popularity suggests the quotes the volume’s editorial team compiled really did meet a public desire, or even need. The Linked Familiarity’s team is using Wikidata data to find out about the people worth quoting in this 55-year stretch, to examine the characteristics that unite them, and to uncover the outliers.
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Original publication information:
Originally published in Pop! Issue No. 2
Date: October 31, 2020
DOI: 10.48404/pop.2020.02
License: CC BY-SA 2.5 CA
Original citation:
Crompton, Constance, Lori Antranikan, Ruth Truong, and Paige Maskell. 2020. “Familiar Wikidata: The Case for Building a Data Source We Can Trust.” Pop! Public. Open. Participatory. 2: n.p. DOI:10.48404/pop.2020.02
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