Collaborative Research: CI-Team Demonstration Project: Enhancing Social Science Research Training with Collaborative Tagging
Investigator: Myron Gutmann
Some of the most profound and persistent questions in modern science relate to understanding the com-plexities of human behavior. Rapidly expanding data and computational capabilities offer the promise of answering these questions that go far beyond the ways that data producers originally envisioned. Yet the very data complexity that makes the potential for analysis so rich poses an enormous challenge. Because the data descriptors are generated only by data producers and disseminators, we face the real risk that only a small group of experts will be able to find and make use of information embedded in large and complex datasets. As a result, the creation of knowledge will go undiscovered by diverse groups of people and organizations, particularly traditionally underrepresented individuals, reducing the information available to the future science and engineering workforce. In other words, the current social science data dissemination infrastructure limits who can participate in the social science workforce, what they can do, and how they can do it.
We propose to tackle the challenge of who participates and how by using existing institutions to develop a community testbed that demonstrates how social scientists can be engaged to be both creators and users of cyberinfrastructure. In particular, we will train students in the use of digital social science data by de-veloping cybertools that can be designed and deployed by both established and new social science com-munities. At the core of our approach is the use of collaborative tagging technology, which will allow student researchers to contribute to the development of a collaborative exegesis or gloss on social science datasets, and consequently to improve their own knowledge and understanding of data, and speed up their ability to be high quality researchers. Collaborative tagging systems allow users to register “tags” (arbitrary strings) with objects such as Web pages or photos and then search on tags registered by themselves or others to locate objects. Although collaborative tagging has proven tremendously popular in mass market settings (e.g., Flickr, del.icio.us) and has been applied successfully in sciences (e.g., Con-notea, CiteULike), there is as yet no evidence to support the hypothesis that collaborative technology can be effective in expanding the access to, use of, and knowledge creation from social science data, which are both more complex and more specialized than the data handled by other tagging systems.
We will collaborate with the University of Chicago and the National Opinion Research Center on this project.
| Funding: | National Science Foundation |
Funding Period: 07/01/2008 to 06/30/2010
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