Cloud-based Scientific Computing

Goals

 * Create software for collaboration between biologists and computer scientists.
 * Enables collaboration through cloud storage and revision history.
 * Flexible lab information management system (easily adapted to other domains)
 * Little to no initial setup costs or expertise required

Background
The recent flood of Web-enabled and Web-based tools for eScience has provided scientists with a wide array of new methods to collect, report, analyze and share their data. These Web-based technologies have demonstrated a great potential to enable broader collaboration and to facilitate the sharing and re- use of experimental data. The wide availability of quality data sets and tested process flows is indeed a welcome addition for an eScientist, yet there are a number of challenges to overcome. In scientific domains, such as bioinformatics, lack of standardization for scientific methods, algorithms, and data sharing can be seen as the greatest challenge for broader adoption.

Many eScience applications, including workflows, have high performance computation requirements. Cloud computing is increasingly seen as a natural choice for such high demand computing tasks because minimal upfront investments in computational resources are required. Using computing clouds also avoids the complications of catering for periodic peak usage, frequent software and hardware updates and the need of trained IT staff.

Plan

 * Use Google Docs API & Software
 * Use Google App Engine

Mike - We could use our own in house servers to talk to Google Docs. This would be an option if Google App Engine just wasn't flexible enough.

Contributors
Michael Cole

Dr. Paul Anderson