This is the home page for the CIVET—Contentious Incident Variable Entry Template—data entry system. CIVET was developed by the NSF-sponsored project titled “A Method for Leveraging Public Information Sources for Social Science Research” which is creating tools to improve the efficiency of data generation in the social sciences, with an initial focus on coding event data in the domain of contentious politics.
CIVET is a standard Django application; the documentation below includes instructions for installing the system either locally or in the Amazon Web Services cloud environment. Django is very widely used and has extensive documentation, so your installation requirements are not covered in the CIVET documentation, it should be relatively easy alternative instructions.
The NSF funding for the project ended on this date. At this point, all of the documented features of the program should be working. However, we are just beginning the process of operational field testing and it is likely—which is to say, inevitable— that some additional bugs will be found, hence this is still considered “Beta-0.9” rather than “1.0.” We currently have two field tests underway, and are hoping to get some additional ones going, and will be posting bug fixes to GitHub promptly as these appear and are resolved.
At present, we have not developed any software for generating the workspace files, though we expect to have at least a couple programs available in the next few months. The problem here is that identifying the various metadata and components in a set of texts is highly specific to the text source, and to date we've not found general solutions for this. As noted in the final chapter of the documentation, over the next year or so we will be seeking additional funding for tool development for these “front-end” tasks, though given the very slow pace of the public funding cycle, this is unlikely to occur until late in 2016 at the earliest. In the meantime, we will be leveraging tools developed in existing projects and, of course, would very much appreciate the contribution of any ancillary tools that the user community develops, particularly for common sources such as Lexis-Nexis, Factiva, ProQuest, LDC Gigaword, and various news feeds and social media.
The development of CIVET is funded by the U.S. National Science Foundation Office of Multidisciplinary Activities in the Directorate for Social, Behavioral & Economic Sciences, Award 1338470 and the Odum Institute at the University of North Carolina at Chapel Hill with additional assistance from Parus Analytics. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.