Standards and Infrastructure for Innovation Data Exchange

This is the author's accepted manuscript. The original publication is available at http://www.sciencemag.org/content/338/6104/196.Economic growth relies in part on efficient advancement and application of research and development (R&D) knowledge. This requires access to data about science, in particular R&D inputs and outputs such as grants, patents, publications, and data sets, to support an understanding of how R&D information is produced and what affects its availability. But there is a cacophony of R&D-related data across countries, disciplines, data providers, and sectors. Burdened with data that are inconsistently specified, researchers and policy-makers have few incentives or mechanisms to share or interlink cleaned data sets. Access to these data is limited by a patchwork of laws, regulations, and practices that are unevenly applied and interpreted (1). A Web-based infrastructure for data sharing and analysis could help. We describe administrative and technical demands and opportunities to meet them. Data exchange standards are a first step

Creating a Data Infrastructure for R&D Workforce Analysis

Atlanta Conference on Science and Innovation Policy 2011To understand how research and development leads to creation of knowledge and then to track the impact of that knowledge requires a comprehensive model of the research ecosystem that incorporates inputs, outputs, activities, and external factors, and the data to support longitudinal and network analysis. To date, most research has focused on those activities and outputs that are readily accessible, including publication output and follow-on citations, as well as patents and patent citations. While these outputs are robust and can be normalized by field of research, additional data are needed to capture important aspects of research not published in journals or patents. Moreover, efforts to assemble systematic information on researchers, including their biographic information, institutions, support, and networks, are in a fledgling stage. We propose a workshop to discuss considerations in creating a data infrastructure to support quantitative analysis of the research workforce, its outputs, and impacts. The workshop is organized around three overarching themes: data linkages, data standards, and data privacy. Each theme will be explored as a moderated conversation among panel participants with implementation expertise, providing perspectives from academic, industry, and non-profit organizations