| Popis: |
Finding and using statistics can be challenging because such information is located in multiple places and exists in large volumes. Efforts such as FedStats (www.fedstats.gov) address the challenge by providing gateways. Our project takes these efforts further by proposing the Statistical Knowledge Network (SKN). We envision a seamless network, where users have transparent access to varied statistical information. The SKN would enable people to find statistics without having to know particular sources, and provide context for understanding and use. Over the last 4 years, we have been developing the SKN: developing a suite of tools for end-users, conceptualizing the architecture, and conducting user studies. In this presentation, we present a status report on our work to date and our future directions. Acknowledgments: Other contributors to this work are Gary Marchionini and Stephanie W. Haas of the University of North Carolina-Chapel Hill, and Ben Shneiderman and Catherine Plaisant, of the University of Maryland-College Park. This material is based upon work supported by the National Science Foundation (NSF) under Grant EIA 0131824. Project information is available at http://ils.unc.edu/govstat. |
