Scientific Data

Scientific Data

The Scientific Data Group is focused on the definition of the infrastructure needed for data intensive computing at scale.  We are particularly interested in connections between experiments and simulations and the need to enable high quality decisions in near-real-time. Our approach is based on deep partnerships with leading science teams, enabling co-design of new algorithms, middleware, and end-to-end systems.



Programming with BIG Data in R: Scaling Analytics from One to Thousands of Nodes

We present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist...

In situ methods, infrastructures, and applications on high performance computing platforms

The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i. e., in situ processing, is due to several...

In Situ Storage Layout Optimization for AMR Spatio-temporal Read Accesses

Analyses of large simulation data often concentrate on regions in space and in time that contain important information. As simulations adopt Adaptive Mesh Refinement (AMR), the data records from a...


  • Chemistry
  • Biology and Genomics
  • Astrophysics, Climate
  • Environmental Science
  • National Security
  • Forensics
  • Simulation Science
  • Epidemiology
  • Fusion Science
  • Transportation and Automotive
  • Health and Safety
  • Grid Technologies
  • Manufacturing
  • Future Combat Systems
  • Remote Sensing
  • Computer Network Security