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Member Update 


The latest news and events from the OCLC cooperative 

        

  
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Join OCLC's Ted Fons and colleagues 
for a one-hour webinar from the Collective Insight series:


Tracing the Evolution of Linked Data


Monday, June 2, 1:00 – 2:00 pm, Eastern Time



Five years ago linked data was introduced to the library community as a 
powerful technology with great promise for libraries, visionary but abstract. 
In the years since, what was a buzzword has become a fixture in our lexicon. 
Yet as our understanding of the technology has progressed and solidified, we 
still struggle to identify the specific benefits of linked data within our 
individual institutions. Join OCLC’s Ted Fons, Corey Harper of New York 
University, and Phil Schreur of Stanford University as they trace the evolution 
of linked data technology over the years, and demonstrate practical 
applications of the latest linked data technologies being utilized in libraries 
today.

Ted will introduce the topic and provide a brief overview on the evolution of 
linked data; Corey and Phil will dig deeper with specific examples that will 
help illustrate this evolution.

Corey will discuss next steps for LOD-LAM (Linked Open Data for Libraries & 
Museums), exploring tools and techniques to process, enrich and aggregate 
metadata. He will touch on recent developments, especially focused on efforts 
within the Hydra and Blacklight community and in the user groups for the Ex 
Libris product suite. Corey will give special attention to the need for use 
cases and experimentation.

Phil will speak on Linked Data for Libraries, a promising new grant created 
through a partnership among Cornell, Harvard and Stanford universities. The 
project goal is creation of a Scholarly Resource Semantic Information Store 
(SRSIS) model that works both within the individual institutions and through a 
coordinated, extensible network of linked open data. Phil will discuss the 
choice of BIBFRAME as the common data format for this project and conversion to 
BIBFRAME from MARC data.
 

                        
                        
                        
                        

  
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