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Evaluating the DataVerse Network: Connecting Open Data with Scholarly Communications

The rise of Big Data and the growing public and federal interest in open and transparent research has resulted in a proliferation of architectures and services designed to facilitate data publication. Starting from a core set of common features, repository solutions are evolving to incorporate an increasing variety of metadata profiles, on-the-fly analysis and streaming capabilities, access control models, and archival and preservation features. This breadth of features and the corresponding need to integrate data publication within existing and often well-established institutional repository services leaves Scholarly Communications Librarians faced with a broad range of choices, challenges, and opportunities.

From this perspective, this session will focus on the University of New Mexico’s evaluation of Harvard and MIT’s DataVerse Network ( as a potential data service to extend and compliment their institutional repository ( and open access journal portal ( Topics and features to be discussed include the DataVerse Network’s branding and front end customization capabilities, extensible metadata model, user access controls and administration. An overview of the new DataVerse plugin for the Open Journal System will also be provided.


Jonathan Wheeler
Jonathan Wheeler is a Data Curation Librarian at the University of New Mexico Libraries in Albuquerque, NM. Prior to joining the faculty at UNM, Jon provided access services at the University of North Carolina at Asheville’s Ramsey Library and served as an archivist and environmental records coordinator for the White Sands Missile Range in southern New Mexico.

Jon’s role in the UNM Libraries includes the development of research data ingest, packaging and archiving work flows, coordinating activities for the Libraries’ Data Archives committee and providing operational support for Data and Outreach Librarians. His research interests include trustworthy and sustainable architectures for long term data preservation and the disposition of research data in response to funding requirements.