====== Background ======

Some time ago, we got together with Terran Lane and Joe Kniss, faculty at UNM, to discuss potential SSDS collaborations we might explore.


===== Joe Kniss =====

Discussion centered on visualization for scientific applications
  * projection, permutation, slicing, filters (for example, volume-rendered images)
    * ad-hoc strides on data, blocked for stride access but different viz needed
  * pattern recognition
  * tends to be bandwidth intensive, I/O bound
  * compression vs. slicing
  * ray-tracing, animation and rendering

What about randomized access pattern requests?  Can we use a metabot to handle these in real-time or near?

Statistical summary in metadata is common

===== Terran Lane =====
Neural imaging data (collaborations with MIND Institute)
  * FMRI
    * scalar measurement over time/spzce
    * variable size data sets (~10GB)
  * raw DTRI
    * tensor measurement
    * each file 2GB
  * applications are multi-stage pipelines of transforms
  * NIH might be interested in funding something if we can generate ideas