DAT: A Visual Analytics Approach to Science and Innovation Policy

University Of North Carolina At Charlotte, Charlotte NC

Investigators

Abstract

A fair amount of work, such as map of science visualization methods, has been done in the area of visualization of scientific discovery and of the relationships among scientific disciplines. Typically, both the maps and the portfolio analyses are derived from keyword and/or citation analyses of research papers coupled with categorizations by discipline of journals and conferences. Although useful, this analysis is far from complete because it does not consider the full text of the papers, just the keywords and citations, and does not consider other sources, such as research project abstracts compiled by funding agencies and reports published by agencies or research organizations. A complete analysis upon which to base policy decisions, evaluations of the effectiveness of funding, or assessments of the direction of a field would include an integrated analysis of all these sources. Intellectual Merit: This project develops a visual analytics approach to perform these assessments of multiple sources, including full text of papers, abstracts, and reports that have not been available before. The approach is exploratory, supporting investigations where one does not initially know precisely what one is looking for but rather uses tools that permit the discovery of new relations and the uncovering of insights. Once found these insights can be looked at in more detail, tested with the gathering of new evidence, and then be the basis of further insight discovery. To support this exploratory investigation, analyses must be, at least in initial stages, unstructured and automated. The most significant words and relations must bubble up from the texts themselves. They must be automated because there will be too many text documents to assess in any other way. Yet, the analyses cannot be completely automated; there must be a place to insert understanding, to organize and make sense of what is found and to direct the investigation in a new direction based on what is found. This is exactly where interactive visual analytics makes its contribution, revealing to investigators detailed results in understandable visual displays, providing clues to prompt further exploration, and supporting organization and annotation of collected evidence and pursuit of new hypotheses. This project also looks at changes and trends over time in the paper and other collections. Detailed examination of changes and trends over time brings out behaviors that may be caused by new or newly revealed directions chosen by researchers in a field, by changes in funding, or by new and important applications. The approach that is applied is based on analyses of streaming text organized into stories, reports, or similar narrative structures. The streaming stories are organized on the fly into ?event clusters? of similar stories that begin and end at particular points in time and have detailed time structures. The goal is to identify motivating events such as new funding directions, new directions established by leaders in a field, as well as new interdisciplinary thrusts across fields. Broader Impacts. As indicated by the recent Visualization of Scientific Discovery Workshop (September, 2008) with sponsorship by NSF, the DOE Office of Science, collaboration by several NSF divisions, and broad attendance by program managers, researchers, and business innovators, there is a significant interest and need for visualization. In addition, the just-released Science of Science Policy Roadmap (November, 2008) from the Office of Science and Technology Policy and the National Science and Technology Council prominently mentions the need for visualization, and in particular visual analytics, tools for science analysis. This report also mentions the need for assessment of these tools for real science analysis applications. This project is positioned to meet both these needs by pursuing the development and assessment of a broad, flexible visual analytics approach for real science and innovation policy applications with real data.