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