Academic Search and Reference Visualization

This project aims to streamline the research process by combining aspects of multiple search, visualization, and reference management systems to allow researchers and practitioners to rapidly discover relevant works and their degree of relatedness.  Using standard graphs like those found on the Microsoft Citation Maps or produced by SAGE allows easy navigation and contextualization, but the extraction of underlying data to form visual themes and flows of impact through edge tracing, h-index, and other measures to form realms of heat maps to represent an authors impact on (a) other authors (b) their field (c) other fields could further enhance the student, scholar, or practitioner’s ability to explore the relationships among works.

I’m exploring reference data through longitudinal views in a force-directed graph, shaded as a heat map, and allowing for multiple renderings to visualize these interconnections.  I certainly have no intention of reinventing the wheel, and will be basing the interactive techniques with other that have been explored by Microsoft Research, and others within the data science community.  The goal is to explore methods where machine learning, natural language processing (NLP), and visualization can enable consumers of research to become more targeted and efficient.

The projects under investigation to incorporate are currently:

While going through possible methods and sources, I did stumble across — a very slick site with a very talented set of individuals contributing to both the art and the science of data-driven intelligence.  I’m certain to learn something from them in the process.



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