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

dc.contributor.advisorKrumwiede, Kimberly Hoggatten
dc.creatorMoser, Courtney Joyen
dc.date.accessioned2010-07-12T18:03:55Zen
dc.date.accessioned2014-02-19T22:02:20Z
dc.date.available2010-07-12T18:03:55Zen
dc.date.available2014-02-19T22:02:20Z
dc.date.issued2007-05-22en
dc.description.abstractGiven 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.digitalOriginborn digitalen
dc.format.mediumElectronicen
dc.format.mimetypeapplication/pdfen
dc.identifier.other761320197en
dc.identifier.urihttp://hdl.handle.net/2152.5/439en
dc.language.isoenen
dc.subjectCooperative Behavioren
dc.subjectData Miningen
dc.subjectResearch Personnelen
dc.titleDefining Social Network Structure Through Text Similarity Analysis: A Model for Promoting Collaboration and Examining Conditions Impacting the Success of Collaborative Endeavors Within a Research Communityen
dc.type.genredissertationen
dc.type.materialTexten
thesis.date.available2008-05-22en
thesis.degree.departmenten
thesis.degree.disciplineBiomedical Communicationsen
thesis.degree.grantorGraduate School of Biomedical Sciencesen
thesis.degree.levelM.A.en
thesis.degree.nameMaster of Artsen

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