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UID:submissions.supercomputing.org_SC23_sess431_misc330@linklings.com
SUMMARY:Scientific Data Democratization: Enabling Efficient Access and Ana
 lysis of Large-Scale Scientific Data
DESCRIPTION:Scott Klasky (Oak Ridge National Laboratory (ORNL))\n\nThe vas
 t amounts of data generated by scientific research hold immense potential 
 for advancing knowledge and discovery. However, the complexity and sheer v
 olume of this data often pose significant challenges in terms of accessibi
 lity, analysis, and interpretation. Scientific data democratization aims t
 o address these challenges by enabling researchers to easily access, analy
 ze, and share scientific data, regardless of their technical expertise or 
 the location of the data.  My group along with our close collaborators hav
 e worked with hundreds of scientific communities in the past 20 years, in 
 many disciplines ranging from astronomy, fusion, combustion, seismology, w
 eather, climate, accelerator science, material science to clinical patholo
 gy. In our partnerships we have created sustainable software components wh
 ich help address the following needs 1) Creating a self-describing I/O fra
 mework which allows data to be read/written at terabytes/sec, 2) Having th
 e ability to query PBs of data efficiently even for derived quantities whi
 ch are NOT contained in the data, 3) Having the ability to subscribe to da
 ta (in memory) without modifying the codes such that I/O is abstracting fr
 om data-at-rest to data-in-motion, 4) Creating new mathematical formulatio
 ns which allows data to be reduced in both the size and in the degrees of 
 freedom to allow for faster access, and 5) having the ability to work with
  federated data, as if it was local. In this presentation, we will explore
  the challenges and opportunities associated with scientific data democrat
 ization, along with some of the work we have done in these fields.\n\nTag:
  Data Analysis, Visualization, and Storage, Data Compression\n\nRegistrati
 on Category: Workshop Reg Pass\n\nSession Chairs: Sheng Di (Argonne Nation
 al Laboratory (ANL), University of Chicago); Dingwen Tao (Institute of Com
 puting Technology, Chinese Academy of Sciences; University of Chinese Acad
 emy of Sciences); Ana Gainaru (Oak Ridge National Laboratory (ORNL)); Jiey
 ang Chen (University of Oregon); Shadi Ibrahim (French Institute for Resea
 rch in Computer Science and Automation (INRIA)); and Xin Liang (Oregon Sta
 te University)\n\n
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