Presentation given by Nirvan Merchant in the afternoon Architecture Forum Session on Day 1, June 24, at the EarthCube All-Hands Meeting

1. Data to Discovery
The iPlant Collaborative
Community Cyberinfrastructure for Life Science
Nirav Merchant (nirav@email.arizona.edu)
iPlant / University of Arizona

2. Data to Discovery
The iPlant Collaborative: Vision
www.iPlantCollaborative.org
Enable life science researchers and educators to
use and extend cyberinfrastructure

3. Data to Discovery
iPlant: Layer Cake

4. Data to Discovery
iPlant Architectural Motivation
• We strive to be the CI Lego blocks
• Danish 'leg godt' - 'play well’
• Also translates as 'I put together' in Latin
• If a solution is not available you can craft
your own using iPlant CI components

5. Data to Discovery
iPlant Atmosphere

6. Data to Discovery
iPlant Data Store

7. Data to Discovery
iPlant BISQUE

8. Data to Discovery
How is it being used ?
• User build their own systems (powered by
iPlant components) but managed by them
• Consume specific components (a la carte,
data store, Atmosphere)
• Directly use applications (DE)
• Custom design appliances (Atmosphere)
• Publish their findings (PNAS, Nature)
• Advocate use
• Create learning material and courses

9. Data to Discovery
9
www.iplantcollaborative.org
iPlant’s role as an enabling CI
Example
“Powered by
iPlant” Impact
CoGE usage
and user count
after federation
and
interoperability
with iPlant
9

10. Data to Discovery
Why is it valuable ?
• Users are able to over come data and
computational bottle necks
• Share data of ANY size with ANYONE
• Connect data and compute on single
platform
• Manage their data and computations
regardless of scale
• Build their own apps and solutions (create
their own community iAnimal, iVirome)
• Create custom appliances

11. Data to Discovery
iPlant: What worked
• All major CI components have seen steady
adoption (few exception)
• “Think tank to do tank” transition was
rapid
• Evolved to a technology proving ground
• Take research products (NSF funded) to
production use for our community
• Running infrastructure is not fun, building
is. Allowing people to focus on science
(while stream line CI)

12. Data to Discovery
iPlant: What worked
• Evolution of training (software carpentry)
• Sharing/collaboration
• Give people exit strategy (options) and
they are happy adopt solution
• Provide feedback to CI component
creators to improve (usability)
• Expectation management: Do not expect
the same experience (cable cord cutting
v/s netflix/hulu)

13. Data to Discovery
What did not work
• Managing distributed teams is harder in
VO (load balancing, enthusiasm etc)
• Technology lifecycle is not synchronized
across all products
• Relying on multiple providers for solution
is challenging (downtimes)
• Changing/Evolving needs of community
are hard to predict
• Growth of users out paces our cloud
capabilities (see tweets)

14. Data to Discovery
Even the tech geeks notice

15. Data to Discovery
Connect with iPlant!
Twitter: @iPlantCollab #iPlant
Facebook: facebook.com/iPlantCollab
LinkedIn: iplant.co/iPlantCollabLinkedIn
Google+: iplant.com/iPlantGooglePlus