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grammar fixes
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README.md
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The images are samples from the [Arkyves database](https://brill.com/view/db/arko?language=en). This collection includes images from
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> from libraries and museums in many countries
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[More Information Needed]
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#### Annotation process
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The annotations are derived from the source dataset see above.
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[More Information Needed]
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#### Who are the annotators?
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### Discussion of Biases
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Iconclass as a metadata standard absorbs biases from the time and place of
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One should be aware of these limitations to Iconclass, and in particular, before deploying a model trained on this data in any production settings.
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The images are samples from the [Arkyves database](https://brill.com/view/db/arko?language=en). This collection includes images from
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> from libraries and museums in many countries, including the Rijksmuseum in Amsterdam, the Netherlands Institute for Art History (RKD), the Herzog August Bibliothek in Wolfenbüttel, and the university libraries of Milan, Utrecht and Glasgow. [source](https://brill.com/view/db/arko?language=en)
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[More Information Needed]
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#### Annotation process
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The annotations are derived from the source dataset see above. Most annotations were likely created by staff with experience with the Iconclass metadata schema.
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#### Who are the annotators?
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### Discussion of Biases
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Iconclass as a metadata standard absorbs biases from the time and place of its creation (1940s Netherlands). In particular, '32B human races, peoples; nationalities' has been subject to criticism. '32B36 'primitive', 'pre-modern' peoples' is one example of a category which we may not wish to adopt. In general, there are components of the subdivisions of `32B` which reflect a belief that race is a scientific category rather than socially constructed.
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The Iconclass community is actively exploring these limitations; for example, see [Revising Iconclass section 32B human races, peoples; nationalities](https://web.archive.org/web/20210425131753/https://iconclass.org/Updating32B.pdf).
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One should be aware of these limitations to Iconclass, and in particular, before deploying a model trained on this data in any production settings.
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