Thesis (M.S.)--Florida State University, 2005. / Advisor: Dr. Sherry Southerland, Florida State University, College of Education, Dept. of Middle and Secondary Education. Title and description from dissertation home page (viewed June 8, 2005). Document formatted into pages; contains viii, 128 pages. Includes bibliographical references.

Thesis (M.Ed.)--University of Hong Kong, 1984. / Also available in print.

Thesis (Ph. D.)--University of California, San Diego, 2006. / Title from first page of PDF file (viewed September 5, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 227-234).

Thesis (M.Ed.)--University of Hong Kong, 1990. / Includes bibliographical references (leaf 75-78). Also available in print.

Thesis (Ph. D.)--Ohio State University, 1970. / Includes vita. Includes bibliographical references (leaves 254-258). Available online via OhioLINK's ETD Center

Thesis (M.I.T.)--The Evergreen State College, 2007. / Title from title screen viewed (6/23/2008). Includes bibliographical references (leaves 149-157).

Thesis (Ph. D.)--Oregon State University, 1999. / Typescript (photocopy). Includes bibliographical references (leaves 510-525). Also available via the World Wide Web.

Thesis (MEd) -- University of South Australia, 1994

<p> Our collective memory, the history that is cultivated through reflection, documentation, and consensus of historical data, is predicated upon the citizenry having access to the historical materials that society has created. Digitization has enabled greater public access to those materials. However, are items being scanned or digitally photographed to create surrogates that are then not made available to the world? The impetus for this study is to delve into whether or not intentional or unintentional personal choices play a role in determining which items archivists transform into digital surrogates; both in the decision of what to digitize and what to make available to the public on the World Wide Web. When one archival collection is prioritized over another or when it is not possible to digitize an entire collection, what rationale is used to determine which items will be digitized and published online? Do intentional or unintentional personal choices come into play in the decision-making? To answer these questions, four case studies were conducted, involving the random sampling of online collections and concomitant interviews of archivists. The purpose of this study is to enhance archivists&rsquo; understanding of the reasons that guide the digitization decision-making process. Through such understanding, archivists can be more proactive in the decision-making process to realize the benefit of digitizing and publishing archival materials that ultimately affect collective memory. The findings of this research revealed that in the case of the four institutions assessed, archivists do use personal choice to determine which materials within an archive are digitized. </p>

<p> Scientific datasets associated with a research project can proliferate over time as a result of activities such as sharing datasets among collaborators, extending existing datasets with new measurements, and extracting subsets of data for analysis. As such datasets begin to accumulate, it becomes increasingly difficult for a scientist to keep track of their derivation history, which complicates data sharing, provenance tracking, and scientific reproducibility. Understanding what relationships exist between datasets can help scientists recall their original derivation history. For instance, if dataset <i>A</i> is contained in dataset <i>B,</i> then the connection between <i>A</i> and <i>B</i> could be that <i>A</i> was extended to create <i>B.</i> </p><p> We present a relationship-identification methodology as a solution to this problem. To examine the feasibility of our approach, we articulated a set of relevant relationships, developed algorithms for efficient discovery of these relationships, and organized these algorithms into a new system called ReConnect to assist scientists in relationship discovery. We also evaluated existing alternative approaches that rely on flagging differences between two spreadsheets and found that they were impractical for many relationship-discovery tasks. Additionally, we conducted a user study, which showed that relationships do occur in real-world spreadsheets, and that ReConnect can improve scientists' ability to detect such relationships between datasets. </p><p> The promising results of ReConnect's evaluation encouraged us to explore a more automated approach for relationship discovery. In this dissertation, we introduce an automated end-to-end prototype system, ReDiscover, that identifies, from a collection of datasets, the pairs that are most likely related, and the relationship between them. Our experimental results demonstrate the overall effectiveness of ReDiscover in predicting relationships in a scientist's or a small group of researchers' collections of datasets, and the sensitivity of the overall system to the performance of its various components.</p>