Given the importance of cross‐disciplinary research (CDR), facilitatingCDReffectiveness is a priority for many institutions and funding agencies. There are a number ofCDRtypes, however, and the effectiveness of facilitation efforts will require sensitivity to that diversity. This article presents a method characterizing a spectrum ofCDRdesigned to inform facilitation efforts that relies on bibliometric techniques and citation data. We illustrate its use by theToolbox Project, an ongoing effort to enhance cross‐disciplinary communication inCDRteams through structured, philosophical dialogue about research assumptions in a workshop setting.Toolbox Project workshops have been conducted with more than 85 research teams, but the project's extensibility to an objectively characterized range ofCDRcollaborations has not been examined. To guide wider application of theToolbox Project, we have developed a method that uses multivariate statistical analyses of transformed citation proportions from published manuscripts to identify candidate areas ofCDR, and then overlays information from previousToolbox participant groups on these areas to determine candidate areas for future application. The approach supplies 3 results of general interest: A way to employ small data sets and familiar statistical techniques to characterize CDR spectra as a guide to scholarship on CDR patterns and trends. A model for using bibliometric techniques to guide broadly applicable interventions similar to the Toolbox. A method for identifying the location of collaborative CDR teams on a map of scientific activity, of use to research administrators, research teams, and other efforts to enhance CDR projects.

UNLABELLED: Microbial communities have an important role in natural ecosystems and have an impact on animal and human health. Intuitive graphic and analytical tools that can facilitate the study of these communities are in short supply. This article introduces Microbial Community Analysis GUI, a graphical user interface (GUI) for the R-programming language (R Development Core Team, 2010). With this application, researchers can input aligned and clustered sequence data to create custom abundance tables and perform analyses specific to their needs. This GUI provides a flexible modular platform, expandable to include other statistical tools for microbial community analysis in the future. AVAILABILITY: The mcaGUI package and source are freely available as part of Bionconductor at http://www.bioconductor.org/packages/release/bioc/html/mcaGUI.html