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Proceedings of the 7th International Symposium on Wikis and Open Collaboration, 2011, Mountain View, CA, USA, October 3-5, 2011; 01/2011
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Analyzing Microtext, Papers from the 2011 AAAI Workshop, San Francisco, California, USA, August 8, 2011; 01/2011
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Proceedings of the 7th International Symposium on Wikis and Open Collaboration, 2011, Mountain View, CA, USA, October 3-5, 2011; 01/2011
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Statistical Analysis and Data Mining. 01/2010; 3:126-139.
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Proceedings of the 2009 International Symposium on Wikis, 2009, Orlando, Florida, USA, October 25-27, 2009; 01/2009
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International Conference on Computational Aspects of Social Networks, CASoN 2009, Fontainebleau, France, 24-27 June 2009; 01/2009
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The 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2009, Washington DC, USA, November 11-14, 2009; 01/2009
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Collaborative Computing: Networking, Applications and Worksharing, 4th International Conference, CollaborateCom 2008, Orlando, FL, USA, November 13-16, 2008, Revised Selected Papers; 01/2008
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Proceedings of the 3rd International Conference on Collaborative Computing: Networking, Applications and Worksharing, White Plains, New York, USA, November 12-15, 2007; 01/2007
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ABSTRACT: The concept of scientific mashups is gaining popularity as the sheer amount of scientific content is scattered over dif-ferent sources, such as databases or public websites. A vari-ety of mashup development frameworks exist, but none fully address the needs of the scientific community. One limita-tion of scientific mashups is the issue of trust and attribute; especially when the content comes from collaborative in-formation repositories where the quality of such content is unknown. In this paper, for our case study we focus on CalSWIM whose content is taken from both highly reliable sources and Wikipedia which may be less so. We will show how integrating CalSWIM with a reputation management system can help us assess the reputation of users and the trustworthiness of the content. Using user reputations, the system selects the most recent and trustworthy revision of the wiki article rather than merely the most recent revision, which might be vandalistic or of poor quality.