“Two Perspectives on Commuting and Workplace: A Microdata Comparison of Home to Work Flows Across Linked Survey and Administrative Files,” Andrew Green (U.S. Census Bureau, Cornell University), Mark Kutzbach (U.S. Census Bureau), Lars Vilhuber (U.S. Census Bureau, Cornell University)
“Crowdsourcing Codebook Enhancements: A DDI-based Approach”
Benjamin Perry (Cornell University), Venkata Kambhampaty (Cornell University), Kyle Brumsted (McGill University), Lars Vilhuber (Cornell University), William Block (Cornell University)
“Formal Privacy Protection for Data Products Combining Individual and Employer Frames”, Ashwin Machanavajjhala (Duke University), Samuel Haney (Duke University), Matthew Graham (U.S. Census Bureau), Mark Kutzbach (U.S. Census Bureau), Lars Vilhuber (Cornell University and U.S. Census Bureau), John Abowd (Cornell University and U.S. Census Bureau)
Benjamin Perry, Venkata Kambhampaty, Kyle Brumsted, Lars Vilhuber, & William C. Block: “Crowdsourcing Codebook Development and Enhancements in CED²AR”
Abstract: Recent years have shown the power of usersourced information evidenced by the success of Wikipedia and its many emulators. This sort of unstructured discussion is currently not feasible as a part of the otherwise successful metadata repositories. Creating and augmenting metadata is a laborintensive endeavor. Harnessing collective knowledge from actual data users can supplement officially generated metadata. As part of our Comprehensive Extensible Data Documentation and Access Repository (CED²AR) infrastructure, we demonstrate a prototype of crowdsourced DDI on actual codebooks. While the system itself is more general, the demonstrated implementation relies on a set of linked deployments of the basic software on web servers. The backend transparently handles changes, and frontend has the ability to separate official edits (by designated curators of the data and the metadata) from crowdsourced content. The implementation allows a data curator, such as a statistical agency, to collect and incorporate improvements suggested by knowledgeable users in a structured way.