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What Google Maps can do for biomedical data dissemination: examples and a design study.

Radu Jianu¹, David H Laidlaw

¹School of Computing and Information Sciences, Florida International University, 11200 SW 8th St, Miami, FL 33199, USA. rdjianu@cis.fiu.edu

BMC Research Notes

|May 7, 2013

Summary

This summary is machine-generated.

Biologists can now explore unfamiliar scientific datasets more easily using Google Maps-based visualizations. This approach offers accessible, multi-scale views, reducing analysis time and data loss.

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Area of Science:

Biomedical data visualization
Bioinformatics
Scientific data exploration

Background:

Assessing unfamiliar biological datasets is challenging due to time constraints and complex software.
Current methods for preliminary data assessment can be unwieldy or time-consuming.
This can lead to discarding potentially valuable biological data.

Purpose of the Study:

To explore design opportunities using the Google Maps interface for biomedical data visualization.
To develop low-overhead, expressive biological visualizations for multi-scale data exploration.
To leverage the Google Maps API for accessible and intuitive biological data analysis.

Main Methods:

Utilized Google Maps API to display pre-rendered biological visualizations in browsers.
Implemented sparse yet powerful interactions for data exploration.
Developed five example visualizations: gene co-regulation, heatmap, genome browser, protein interaction network, and brain white matter.

Main Results:

Google Maps visualizations provide accessible, scale-dependent perspectives for unfamiliar datasets.
The familiar Google Maps interface enhances user attraction and usability, especially for less experienced users.
The five implemented visualizations offer novel design elements for biological visualization developers.

Conclusions:

A low-overhead approach enables biologists to access analyzed views of unfamiliar scientific datasets.
Pre-computed visualizations with intuitive interactions distributed via Google Maps framework are effective.
The study provides an evaluation, design guidelines, and concrete examples for this visualization approach.