The Delaware Valley Industrial Resource Center (DVIRC), in collaboration with the Battelle Center for Mathematics & Science Education Policy at Ohio State University, the Ben Franklin Technology Partners of Southeastern PA and the 21st Century Partnership for STEM education, is examining how an advanced data visualization tool can accelerate and deepen regional partnerships designed to spur innovation in STEM-based industries and education. The project focuses on: (1) assessing the potential of advanced visualization tools using a robust sample of regional STEM partnership data records, (2) catalyzing the formation of a regional research and applications partnership around visual analytics in support of STEM education and innovation, and (3) developing a visual analytics research and development agenda that is responsive to academic, business and educational partners in the region. The primary work is to test an advanced data visualization tool - Starlight - to learn how it can organize and depict research, data, personnel and institutions in a dynamic visual environment to map connectivity between STEM assets. Ultimately, the project explores how to operationally and intellectually link tri-state regional assets into a dynamic network that supports a robust STEM innovation ecosystem. The robustness of a regional innovation ecosystem depends on the degree to which purposeful and dynamic connections are made between and among ideas, institutions and the individuals who work within this ecosystem - those engaged in university and private research, commercialization, business and finance, government and K-20 education. The making of such connections is driven by need and/or opportunity, as conditioned by situational awareness and the social, political and cultural characteristics of the regional ecosystem. The project is studying how visualization tools can help regional actors surmount the barriers to effective situational awareness that are caused by the plethora of actors and resultant large amounts of dispersed data.
Among the questions being asked in this project are:
How can we best understand, organize, and utilize a region's STEM assets, including those funded by the NSF, to spur educational and economic innovation? How can Starlight be used to organize complex sets of information (STEM assets) to improve economic performance and accelerate the development of regional social capital?
How do the characteristics of key asset classes in the region compare to the characteristics of vibrant regional innovation centers?
What (a) visualization outputs and (b) user interfaces with the tools facilitate productive, collaborative engagement by key stakeholders with the process and outputs of visual analysis and mapping? Can we identify a common set of user interface characteristics that is effective across a wide range of stakeholder segments?
In large metropolitan regions, the number of institutions, actors and activities associated with science, technology engineering and math (STEM) can be vast. Innovations and efficiencies regularly come about through both planned and unexpected connections leading to productive synergies. Yet to comprehend the complexity of the actual and potential linkages between these STEM related education, research and business assets is staggering. This two-year EAGER grant explored how advanced visual analytics might help actors organize and understand a region's STEM assets to spur innovation. Visualization often allows the viewer to see more quickly certain characteristic of data sets than what could be seen only after a more lengthy analysis. Specifically, we explored the usefulness of "Starlight" Visualization software to map regional STEM assets. The project further explored how visualization tools can help regional institutions achieve heightened awareness to more effectively partner with the plethora of STEM related actors and programs, while utilizing large amounts of dispersed data. For example, visual analytics might show there is an abundance of STEM programming targeted at boys and a dearth of programming to support girls and underrepresented populations, Cataloguing regional assets is a common mapping approach. In this context, we regarded "assets" as referring to not only to people and institutions, but also information and knowledge. While the task of forming lists or electronic databases of STEM assets may be more or less laborious, it is conceptually straight forward. To be sure, cataloging assets has some value, but what is of more value is being able to "see" relationships between assets in more or less real time. Thus, for end users the goal is to portray assets in ways that: 1) are up-to-date; 2) show the dynamic linkages between and among entities, programs, individuals and knowledge; 3) provide for an intuitive grasp of key themes and relationships allow for collaborative analysis and sense-making. We first spent considerable time conceptualizing the meaning of STEM assets and their dynamic nature. We then looked at database structures and methods of capturing asset data. We explored how information about STEM assets can be combined, manipulated and displayed in ways that make sense to users; and how such displays might be used by various actors and organizations. Summary of Findings—Visual Analytics & Asset Mapping 1. A visual analytics package with the breadth and depth of Starlight presents a high learning curve for novice users and offers layers of complexity that far exceeded the requirements of the investigation. a. In an age of "big data," visual analytics tools such as Starlight can help users "see" relationships among data elements better than linear or tabular presentations. While STARLIGHT is a robust software package, less robust packages such as Tableau would likely suffice. PC and Web based data visualization tools, such as Google (while at a generally early stage of broad availability, adoption and use, with various tools having different features, limitations and degrees of ease of use) may offer a better option. b. It is important to try to understand how various software tools differ and to determine which tool can best serve the goals of a data visualization project. Decision factors include in learning time, budgetary limitations, data structure and the software tool's functional capacity. 2. Too little attention has been paid to the interest and requirements of various users. a. Asset mapping can be important for STEM education and economic innovations and activities, but too little attention has been paid to the user interface and to the methodologies needed to keep the data current. Addressing these elements is critical if users are to understand and utilize their assets, particularly as they relate to social capital development. b. By using the PA STEM Asset Mapping database as a test case, we confirmed the need for a user frame of reference. While the PA STEM Asset Mapping Initiative sought to capture potential audiences for each asset, we found insufficient attention paid to how a given class of users would put particular information into practice. The cumbersome structure of the PA database also exposed a major deficiency in the approach: no way to explore the connections between or among assets—people, projects, programs, funding sources, institutions or actors. 3. When visually mapping data, the structure of the data is critical. This pertains to the collection of data; the relationship of data fields; and the taxonomy of the data. When there are efforts to collect and visually map new data sets, the structure and format of the resulting data should be given significant time and reflection during a data structure design process prior to the collection and mapping of the data. The data structure should take into account the ability of people to supply the data, and the anticipated end use of the data, specifically as it relates to visually mapping the data.
