AHM 2014: The iPlant Collaborative, Community Cyberinfrastructure for Life Science
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
4. Data to Discovery
iPlant Architectural Motivation
⢠We strive to be the CI Lego blocks
⢠Danish 'leg godt' - 'play wellâ
⢠Also translates as 'I put together' in Latin
⢠If a solution is not available you can craft
your own using iPlant CI components
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
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)
⢠âThink tank to do tankâ 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)