Mirador is a tool for visual exploration of complex datasets. It enables users to discover correlation patterns and derive new hypotheses from the data.
Mirador is an open source project released under the GNU Public License v2. It is the result of a collaboration between the Sabeti Lab at Harvard University, the Broad Institute of MIT and Harvard, and Fathom Information Design. Initial support was provided by the Center of Communicable Disease Dynamics and the MIDAS network funded by the National Institutes of Health.
Ebola prognosis prediction—Computational methods for patient prognosis based on available clinical data—June 9th, 2015
Ebola data release—De-identified clinical data from Ebola patients treated at the Kenema Government Hospital in Sierra Leone between May and June of 2014—February 26th, 2015
Awards from the Department of Health and Human Services—Mirador received the third place, innovation and scientific excellence awards in the HHS VizRisk challenge—January 5th, 2015
Winning entries in the Mirador Data Competition—Read about the winning correlations submitted by Mirador users—December 1st, 2014
Marriage, Health, and Jobs—Finding associations in the BRFSS dataset—October 27th 2014
Finding and ranking correlations—Video tutorial—October 21st 2014
Loading and viewing data—Video tutorial—October 21st 2014
Mirador Data Competition—Explore public data and win some prizes for your discoveries—Completed: September 28th and November 4th, 2014
Network representation of correlation data—tutorial showing how to combine Mirador's output with Python scripts and other visualization tools to generate network representations of correlation matrices—September 30th 2014
Statistical modeling with Mirador—tutorial on how to use Mirador in Machine Learning—September 27th 2014
Finding correlations in complex datasets—process post about design and development of Mirador—June 18th 2014
Quantitatively measuring correlations—post describing measures of correlation based on Mutual Information—March 25th 2014
Visually representing correlations—post discussing the use of eikosogram plots to represent conditional dependency—January 15th 2014