Understanding how organizations promote innovation is a key element of advancing the science of innovation, and hence the science of innovation policy. Data on innovation inputs, innovation processes, and innovation outputs are increasingly being captured and stored electronically. A number of fundamental bottlenecks to using these data to advance social science research exist due to unsolved issues of privacy, data integration, and data quality. The core scientific challenge is how to make such real-world, large-scale data available to researchers to nurture innovation and perform valid experimentation, while maintaining data privacy. Fortunately, computer scientists have been developing a variety of techniques and building new tools that manage large data sets in ways that can potentially help in supporting and measuring innovation activities.

This workshop brings together social scientists, the users of data on innovation, together with computer scientists, the creators of new tools for collecting data while protecting privacy concerns. The workshop includes leading computer scientists with specialties in data management, data mining, security/privacy and social networks as well as social/organizational scientists, such as economists, sociologists, psychologists and anthropologists.

The focus of the workshop is to identify emerging major challenges in this interdisciplinary area. Three different types of data critical to the study of innovation are the focus of study: third party data, such as U.S. Census data, patendt databases, NSF funding data or citation databases; detailed insider data such as internal communications, team video, or team documentation; and broader insider data such as cross-firm surveys.

The broader impact of the workshop is substantial. First, an enhanced empirical basis for studying innovation is necessary to guide policy decisions. Second, the workshop participants represent a broad variety of disciplines, which creates a broader interdisciplinary network of study. Finally, the attendance of graduate students advances the goal of training a new cohort of researchers in the field.

Project Start
Project End
Budget Start
2009-09-15
Budget End
2010-08-31
Support Year
Fiscal Year
2009
Total Cost
$50,312
Indirect Cost
Name
University of Pittsburgh
Department
Type
DUNS #
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213