Increasingly in basic research and many other sectors, there is a need for quick-look visualization of large datasets in order to (a) conceptualize the nature of the data, and (b) begin identifying data patterns to guide more computationally costly deep-dive analysis. High-end tools for data basing and graphing exist, but usually entail a large overhead in time and effort associated with: importing the data; correctly specifying meta-data; keeping track of a large number of variable names; issuing and scripting commands for graphing; plotting pairwise variables over many iterations; filtering out bad data; re-rendering plots to restricted data ranges; representing multiple variables at once; and so on. This team has developed Filtergraph, a web based tool that allows rapid and intuitive visualization of massive, multi-dimensional datasets.
The web-based tools developed by this team will allow researchers to instantly visualize any dataset in many dimensions at once, and will permit researchers to move through the data rapidly, and quickly filter and slice them. This visual interaction will occur without the need for cumbersome meta-data specification, without the need to remember details of the data structure, and without the need to write scripts to render sophisticated visualizations. The tools developed will be a means to an end: A new, highly visual way of transcending preconceptions about the data and instead let the data speak for themselves. This mechanism would free researchers to creatively and collaboratively engage their expert intuition as they visually and rapidly explore massive datasets for unexpected patterns or outliers that signal important discovery.
