cRIsp: Crowdsourcing Representation Information to Support Preservation

Abstract

In this paper, we describe a new collaborative approach to the collection of representation information to ensure long term access to digital content. Representation information is essential for successful rendering of digital content in the future. Manual collection and maintenance of representation information has so far proven to be highly resource intensive and is compounded by the massive scale of the challenge, especially for repositories with no format limitations. This solution combats these challenges by drawing upon the wisdom and knowledge of the crowd to identify online sources of representation information, which are then collected, classified, and managed using existing tools. We suggest that nominations can be harvested and preserved by participating established web archives, which themselves could obviously benefit from such extensive collections. This is a low cost, low resource approach to collecting essential representation information of widespread relevance.

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