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Michael L. Nelson

Researcher at Old Dominion University

Publications -  430
Citations -  9042

Michael L. Nelson is an academic researcher from Old Dominion University. The author has contributed to research in topics: Web page & Digital library. The author has an hindex of 43, co-authored 388 publications receiving 8354 citations. Previous affiliations of Michael L. Nelson include Langley Research Center & University of Oklahoma.

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Proceedings ArticleDOI

Mink: integrating the live and archived web viewing experience using web browsers and memento

TL;DR: Mink, a new web browser extension that provides a different model for integration of the live and archived web by querying the archives dynamically and asynchronously, and can view the extent to which the currently viewed page on the live web has been archived.
Journal ArticleDOI

School food cost-benefits: England.

TL;DR: Modest levels of government investment in the delivery and promotion of healthier school food is likely to yield both short-term and long-term benefits in relation to nutrition, learning, economics and health.

Buckets: Aggregative, Intelligent Agents for Publishing

TL;DR: Old Dominion University and NASA Langley ResearchCenter are developing NCSTRL+ to address the multi-discipline and multi-genre problems of digital libraries, which provides an archive-independent container construct in which all related semantic and synthetic data types and objects can be logically grouped together, archived, and manipulated as a single object.
Journal ArticleDOI

A Scalable Architecture for Harvest-Based Digital Libraries - The ODU/Southampton Experiments

TL;DR: In this paper, the requirements of current and emerging applications based on the Open Archives Initiative (OAI) and emphasizes the need for a common infrastructure to support them are discussed and a design for a scalable and reliable infrastructure that aims at satisfying these requirements is presented.
Book ChapterDOI

Web Archive Profiling Through Fulltext Search

TL;DR: The Random Searcher Model (RSM) is developed to discover the holdings of an archive by a random search walk and the search cost of discovering certain percentages of the archive holdings for various profiling policies under different RSM configurations is measured.