The Cornell CyberCommons: Federated Capabilities for Information Technology in the Conduct of Research

The Cornell CyberCommons for Research draws on expertise and resources drawn from all Cornell campuses to facilitate collaborative projects that span across Cornell and beyond, including:

•    computational resources for both cluster and cloud (CPU and GPU)
•    storage resources for regulated and unregulated data
•    consulting on a range of research computing topics
•    partnering and work on grant-funded projects
 

For WCM-appointed Investigators with High or Moderate Risk Data (as defined in Policy 11.03: https://its.weill.cornell.edu/policies/1103-data-classification)

Contacts for the resources below are provided at the end of this section.

Computation and Storage:  

  • WCM Data Core:  managed access to hosting, storage, and applications
  • Remote Secure Archive:  object repository accessible via S3 or Globus Transfer
  • Other storage with a range of performance, cost, location, and redundancy
  • Institutional access to cloud services such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, including via application container technologies
  • Colocation of faculty-owned servers and clusters in institutional datacenters

Consulting: 

  • Software
    • Access to licenses
    • Development: algorithms, database, general programming
    • Optimization: containerizing, code improvement, workflow orchestration
  • Use of Infrastructure
    • Guidance and optimization for the use of commercial cloud services
    •  Allocation requests on national resources
    • Management for servers and clusters purchased by researchers
  • Security and compliance, including services

Contact for WCM Appointees with Restricted or Confidential Data:

https://weill.cornell.edu/its/services/research-informatics

For Investigators without High or Moderate Risk Data (including Ithaca campus, as defined in policy 5.10: https://www.dfa.cornell.edu/sites/default/files/vol5_10.pdf) 

Contacts for the resources below are provided at the end of this section.

Computation and Storage:

  • Any of the above services plus:
  • Red Cloud - Cornell's self-service, on-premise cloud (make your application cloud ready consulting available)
  • GPU resources for data-intensive analytical techniques, such as Cryo-EM and machine learning. For access to SCU resources, info at http://scu.med.cornell.edu
  • HPC clusters for traditional parallel computational resources

Consulting:

  • Any of the above consulting services are also available for unrestricted data

Contacts (for other than WCM Restricted or Confidential Data):