Big, Slow, and Linked: Toward Distributed and Scalable Data Practices in Archaeology
This paper highlights the social challenges of bringing "Big Data" to archaeology. In the political economy of universities, corporations, and governments, Big Data enjoys a special status because it tends to require and reinforce institutional and information centralization. We often imagine that the research and analytic opportunities promised by Big Data are a function of the economies of scale offered by the centralized aggregation of fungible datasets. However, many forms of archaeological data are small but complicated, collected under very different conditions and sampling protocols. Moreover, archaeology has many different institutional stakeholders that create and manage data, making centralized aggregation more difficult. Because of these complications, this papers advocates a "Slow Data", incrementalist, approach to building Big Data. As illustrated by Open Context and other Linked Open Data providers, larger datasets can be assembled from data contributions scattered across the Web. While Linked Open Data (especially when combined with text-mining) offers paths for data aggregation, bringing together diverse data still involves potentially contestable judgement calls that require specialized knowledge, thought, and labor investments. Therefore, archaeological Big Data needs community-wide commitments to practice more open and more reproducible research.
Cite this Record
Big, Slow, and Linked: Toward Distributed and Scalable Data Practices in Archaeology. Eric Kansa, Sarah Kansa. Presented at The 81st Annual Meeting of the Society for American Archaeology, Vancouver, British Columbia. 2017 ( tDAR id: 429453)
This Resource is Part of the Following Collections
Abstract Id(s): 14634