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Large-scale collaboration systems often separate their content from the deliberation around how that content was produced. Surfacing this deliberation may engender trust in the content generation process if the deliberation process appears fair, well-reasoned, and thorough. Alternatively, it could encourage doubts about content quality, especially if the process appears messy or biased. In this paper we report the results of an experiment where we found that surfacing deliberation generally led to decreases in perceptions of quality for the article under consideration, especially -but not only -if the discussion revealed conflict. The effect size depends on the type of editors' interactions. Finally, this decrease in actual article quality rating was accompanied by self-reported improved perceptions of the article and Wikipedia overall.

Social media has become globally ubiquitous, transforming how people are networked and mobilized. This forum explores research and applications of these new networked publics at individual, organizational, and societal levels. --- Shelly Farnham, Editor

Association mapping studies promise to link DNA mutations to gene expression data, possibly leading to innovative treatments for diseases. One challenge in large-scale association mapping studies is exploring the results of the computational analysis to find relevant and interesting associations. Although many association mapping studies find associations from a genomewide collection of genomic data to hundreds or thousands of traits, current visualization software only allow these associations to be explored one trait at a time. The inability to explore the association of a genomic location to multiple traits hides the inherent interaction between traits in the analysis. Additionally, researchers must rely on collections of in-house scripts and multiple tools to perform an analysis, adding time and effort to find interesting associations. In this paper, we present a novel visual analytics system called GenAMap. GenAMap replaces the time-consuming analysis of large-scale association mapping studies with exploratory visualization tools that give geneticists an overview of the data and lead them to relevant information. We present the results of a preliminary evaluation that validated our basic approach.