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What people are saying, Resources | 15 May 2017

SHARE Featured in D-Lib Magazine

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D-Lib Magazine

The May/June 2017 issue of D-Lib Magazine includes an article on SHARE’s schema-agnostic approach to aggregating diverse and distributed metadata about scholarship in order to facilitate discovery and innovation. This issue of D-Lib is devoted to projects related to the national digital platform, a framework that the US Institute of Museum and Library Services (IMLS), its grantees, and other stakeholders are using to guide activities and investments in digital infrastructure for libraries and archives. The article about SHARE is authored by the SHARE Operations Team: Cynthia Hudson-Vitale, Rick Johnson, Judy Ruttenberg, and Jeffrey Spies.

The authors note that metadata standards vary widely by discipline or domain, which makes it difficult to discover works that are related to one another across these boundaries. Linking related research across discipline and domain can help make works discoverable in new ways and can help scholars see new connections. It is impractical to ask varied data sources to adopt and implement a common metadata format when the incentives for doing so are low. Instead, SHARE is gathering, standardizing, and linking metadata into an aggregated, open, data set describing a wide range of research outputs.

SHARE’s open data set about diverse types of scholarship is demonstrating the power of a public goods database to inform users of research activity and to foster discovery. The authors conclude, “SHARE is focusing efforts on growing its community of collaborators in order to distribute curation, support, development, and maintenance. Direct collaboration also enables community members to shape solutions like the SHARE data set to best realize their own objectives now and in the future.”

Read the article, “SHARE: Community-focused Infrastructure and a Public Goods, Scholarly Database to Advance Access to Research.”

By Kaylyn Groves
202-296-2296
kaylyn@arl.org
Tags metadata, linked open data, linked data
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