An Overview of the Virtual Data Center Project and Software
Software is now superseded by Dataverse.
Publication information
Micah Altman, Leonid Andreev, Mark Diggory, Gary King, Daniel L. Kiskis, Elizabeth Kolster, Michael Krot, and Sidney Verba. 2001. "An Overview of the Virtual Data Center Project and Software". JCDL '01: First Joint Conference on Digital Libraries, Pp. 203–4.
Abstract
In this paper, we present an overview of the Virtual Data Center (VDC) software, an open-source digital library system for the management and dissemination of distributed collections of quantitative data. (See Dataverse.) The VDC functionality provides everything necessary to maintain and disseminate an individual collection of research studies, including facilities for the storage, archiving, cataloging, translation, and on-line analysis of a particular collection. Moreover, the system provides extensive support for distributed and federated collections including: location-independent naming of objects, distributed authentication and access control, federated metadata harvesting, remote repository caching, and distributed "virtual" collections of remote objects.
For the current open-source research data repository software that continues this line of work, see Dataverse on this site.
See Also
- [Software] An Overview of the Virtual Data Center Project and Software (2001)
- [Paper] An Introduction to the Virtual Data Center Project and Software (2001)
- [Paper] A Digital Library for the Dissemination and Replication of Quantitative Social Science Research (2001)
- [Paper] From Preserving the Past to Preserving the Future: The Data-PASS Project and the Challenges of Preserving Digital Social Science Data (2009)
- [Presentation] Statistically Valid Inferences from Privacy Protected Data (Pew Research Center) (2025)
- [Presentation] Noisy Data from the Noisy Census (Center for Discrete Mathematics and Theoretical Computer Science, Rutgers University) (2022)
- [Presentation] Statistically Valid Inferences from Privacy Protected Data (Harvard, Privacy Tools Project) (2020)
- [Presentation] Statistically Valid Inferences from Privacy Protected Data (Webcast, Project TIER) (2020)