SciServer development proceeds along two parallel lines: System Development, and Outreach Development. In the first phase of development, the System Foundations are established as the Outreach and Educational framework is developed and deployed. In the second phase, during System Scale Out, Educational and Outreach programs expand to take advantage of its increased capabilities.

  System Foundation 2014-2015 Outreach and Education 2014-2018
2014
Consolidate existing systems and components.
Re-Engineer existing core system components, and in particular the CASJobs application.
Extend CASJobs to support MyScratch capability, and thus support the Open Numerical Laboratory concept.
Pilot the Open Numerical Laboratory using Turbulence and Cosmology data sets.
Extend the SciDrive drop-box-like interface to work with CASJobs.
Pilot SciDrive with Earth Science sensor data.
Take Ownership of SDSS2 Data and operations, subsuming them into re-engineered foundation system.
Engage End Users systematically in all aspects of system requirements, testing and acceptance, through managed user groups and workshops. This activity occurs throughout the project.
Communicate with Scientific Communities to ensure that the vision, objective and value of the project are clear.
Develop a “Student Notebook” to provide a framework for developing teacher managed student lessons plans based around the scientific data sets in the system.
2015
  System Scale Out 2016-2018
2016
Enhance System Capability systematically using the above foundations to operate at larger scale.
Develop Scientific Integration with Genomics, Connectomics and Oceanographic data across several dimensions: storage capability and optimization, database parallelization, processing parallelization, and scalable query framework.
Take Ownership of SDSS3 Data and operations, subsuming them into the core framework.
Support Long Tail Science by developing data and metadata handling capabilities.
Develop Capability to Integrate Datasets across large simulation data, large experimental and observational datasets, heterogeneous unstructured long tail data sets, and remote inter-operable data sets residing in alternate source locations.
2017
2018