The South Asia sub-region is now in the forefront of the Open Access movement within developing countries in the world, with India being the most prominent partner in terms of its successful Open Access and Digital Library initiatives. Institutional and policy frameworks in India also facilitate innovative solutions for increasing international visibility and accessibility of scholarly literature and documentary heritage in this country. This publication has its genesis in the recommendations and proceedings of UNESCO-supported international conferences and workshops including the 4th International Conference of Asian Digital Libraries (ICADL2001, Bangalore); the International Conferences on Digital Libraries (ICDL2004 & ICDL2006, New Delhi); and the International Workshop on Greenstone Digital Library Software (2006, Kozhikode), where many information professionals of this sub-region demonstrated their Digital Library and Open Access initiatives. This book describes successful digital library and open access initiatives in the South Asia sub-region that are available in the forms of open courseware, open access journals, metadata harvesting services, national-level open access repositories and institutional repositories. This book may be considered an authoritative Source-book on Open Access development in this sub-region.

University Libraries, with the dawn of Internet era, are compelled to provide relevant information essential to its end users within a short span of time either from its in-house holdings or from resources available in other libraries. This could be made possible only by way of Library Consortia (LC). This paper discusses the planning of LC among the major Campus Libraries of University of Madras. It identifies the need, prerequisites, problems and solutions involved in consortia formation.

Thesis (Ph. D.)--University of North Texas, Dec., 2008. / Title from title page display. Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2007. / "Graduate Program in Communication, Information, and Library Studies." Includes bibliographical references (p. 183-194).

Thesis (Ph. D.)--Stevens Institute of Technology, 1997.

Thesis (Ph. D.)--Princeton University, 1997.

Thesis (Ph.D.)--OGI School of Science & Engineering at OHSU, 2005. / Includes bibliographical references (leaves 112-122).

As more and more output from research institutions is born digital, a means for capturing and preserving the results of this investment is required. To begin to understand and address the problems surrounding this task, Hewlett-Packard Laboratories collaborated with MIT Libraries over two years to develop DSpace, an open source institutional repository software system. This paper describes DSpace in the context of the Open Archival Information System (OAIS) reference model. Particular attention is given to the preservation aspects of DSpace, and the current status of the DSpace system with respect to addressing these aspects. The reasons for various design decisions and trade-offs that were necessary to develop the system in a timely manner are given, and directions for future development are explored. While DSpace is not yet a complete solution to the problem of preserving digital research output, it is a production-capable system, represents a significant step forward, and is an excellent platform for future research and development.

Achieving interoperability between digital libraries and visualization tools is a difficult problem. To solve this problem, a version of the Open Archives Initiative (OAI) Protocol for Metadata Harvesting called VIDI is proposed. It is a lightweight protocol, which contains only 5 request verbs -- Identify, ListMetadataFormats, ListVisdataFormats, ListTransformers, and RequestResultSet. It is extended from the OAI protocol, which enables its simplicity and wider acceptability. It is flexible, which avoids a rigid architecture in implementation. It is general, so it can apply to all kinds of Visualization Systems and Digital Libraries. But most importantly, it reaches our goal of enabling operability between Visualization Systems and Digital Libraries. The protocol design and implementation details are given. Two prototype systems are implemented to demonstrate the above features. Implementation details are given about ENVISION-ODL and ENVISION-MARIAN. Analysis, evaluation, and conclusions reinforce the discussion of the benefits of VIDI. / Master of Science

Automatic text classification using current approaches is known to perform poorly when documents are noisy or when limited amounts of textual content is available. Yet, many users need access to such documents, which are found in large numbers in digital libraries and in the WWW. If documents are not classified, they are difficult to find when browsing. Further, searching precision suffers when categories cannot be checked, since many documents may be retrieved that would fail to meet category constraints. In this work, we study how different types of evidence from multiple sources can be intelligently fused to improve classification of text documents into predefined categories. We present a classification framework based on an inductive learning method -- Genetic Programming (GP) -- to fuse evidence from multiple sources. We show that good classification is possible with documents which are noisy or which have small amounts of text (e.g., short metadata records) -- if multiple sources of evidence are fused in an intelligent way. The framework is validated through experiments performed on documents in two testbeds. One is the ACM Digital Library (using a subset available in connection with CITIDEL, part of NSF's National Science Digital Library). The other is Web data, in particular that portion associated with the Cadê Web directory. Our studies have shown that improvement can be achieved relative to other machine learning approaches if genetic programming methods are combined with classifiers such as kNN. Extensive analysis was performed to study the results generated through the GP-based fusion approach and to understand key factors that promote good classification. / Ph. D.