dc.contributor.advisor	Krumwiede, Kimberly Hoggatt	en
dc.creator	Moser, Courtney Joy	en
dc.date.accessioned	2010-07-12T18:03:55Z
dc.date.available	2010-07-12T18:03:55Z
dc.date.issued	2007-05-22
dc.description	The file named "mosercourtney.pdf" is the primary dissertation file. Additional supplemental files (tables/figures) are also available.	en
dc.description.abstract	Given the breadth and sheer volume of accumulated scientific knowledge, individual researchers often lack the requisite knowledge and resources to adequately address increasingly complex problems; therefore, many researchers are realizing the advantages afforded by collaborative research practices. The application of text data mining technologies to social networking strategies provides a novel approach to identifying opportunities for scientific collaboration through text similarity analysis, provided by the computer program eTSNAP. The data set submitted to eTSNAP comprised 137 research abstracts representing individual scientists affiliated with the Regional Centers of Excellence in Biodefense and Emerging Infectious Diseases. Examination of the data in the form of tables, matrices, and interactive similarity network maps revealed the presence of eight discrete clusters of individuals, linked by the similarity of their abstracts. Further analysis of structural and functional characteristics of each cluster permitted the selection of a single cluster with the highest probability of collaborative success to serve as the pilot cluster. Members of this pilot cluster, renamed the "anthrax cluster" in reference to the common theme of research, received an introductory packet of information explaining the design of the project and soliciting participation in a preliminary survey, developed with intentions of assessing collaborative readiness and garnering practical information to assist in the preparation of a future teleconference. When multiple requests failed to elicit an adequate response, further attempts at establishing collaborative relationships between these researchers merely represented an exercise in futility. Evaluation of this project ultimately consisted of a secondary telephone interview with cluster members along with an in-depth literature review; both components of the final evaluation endeavored to isolate and examine factors that facilitate or inhibit collaboration within a research environment. Results suggest that similar interests alone cannot sustain successful collaboration; rather, complex interactions between a multitude of interconnected variables essentially determine collaborative outcomes.	en
dc.format.digitalOrigin	born digital	en
dc.format.medium	Electronic	en
dc.format.mimetype	application/pdf	en
dc.identifier.oclc	761320197
dc.identifier.uri	https://hdl.handle.net/2152.5/439
dc.language.iso	en	en
dc.subject	Cooperative Behavior	en
dc.subject	Data Mining	en
dc.subject	Research Personnel	en
dc.title	Defining Social Network Structure Through Text Similarity Analysis: A Model for Promoting Collaboration and Examining Conditions Impacting the Success of Collaborative Endeavors Within a Research Community	en
dc.type	Thesis	en
dc.type.genre	dissertation	en
dc.type.material	Text	en
thesis.date.available	2008-05-22
thesis.degree.department	Graduate School of Biomedical Sciences	en
thesis.degree.discipline	Biomedical Communications	en
thesis.degree.grantor	UT Southwestern Medical Center	en
thesis.degree.level	Masters	en
thesis.degree.name	Master of Arts	en

Defining Social Network Structure Through Text Similarity Analysis: A Model for Promoting Collaboration and Examining Conditions Impacting the Success of Collaborative Endeavors Within a Research Community

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