Mashups and Matters of Concern: Generative Approaches to Digital Collections
This article discusses two practical experiments in remaking collections. Drifter (2016) and Succession (2014) build on the affordances of machine-readable collections and APIs to harvest large datasets from diverse sources, and show how these sources can be re-deployed to address complex spatiotemp...
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| Format: | Article |
| Language: | English |
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Open Library of Humanities
2018-04-01
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| Series: | Open Library of Humanities |
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| Online Access: | https://olh.openlibhums.org/article/id/4483/ |
| Summary: | This article discusses two practical experiments in remaking collections. Drifter (2016) and Succession (2014) build on the affordances of machine-readable collections and APIs to harvest large datasets from diverse sources, and show how these sources can be re-deployed to address complex spatiotemporal sites. These projects demonstrate the potential of a mashup-like generative approach based on sampling and recombination. Such approaches generate an expansive range of unforeseeable outcomes, while retaining a highly authored character. Here these projects are analysed through three key constituents: the troublesome trace of data; their extraction of digital samples; and their generative recomposition of samples into emergent outcomes. These techniques remake collections in a way that addresses the intrinsically complex, entangled and heterogeneous nature of what Latour terms ‘matters of concern’. |
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| ISSN: | 2056-6700 |