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DTSTAMP:20260605T154543Z
LOCATION:Bldg. 8 - Room B 101
DTSTART;TZID=Europe/Stockholm:20260629T160000
DTEND;TZID=Europe/Stockholm:20260629T180000
UID:submissions.pasc-conference.org_PASC26_sess126@linklings.com
SUMMARY:MS2G - Data Management in Scientific Workflows
DESCRIPTION:Organizer(s): Florine Willemijn de Geus (CERN, University of T
 wente), Vincenzo Eduardo Padulano (CERN), and Ana-Lucia Varbanescu (Univer
 sity of Twente)\n\nData-driven research methods have become essential for 
 many scientific domains, such as earth sciences, high-energy physics, mate
 rial science, or biomedical engineering. Within these domains, data is col
 lected with different tools, stored in different formats and systems, orga
 nized and accessed in typical workflows, and overly-tuned towards the doma
 in requirements. Additionally, the data-management practices by which data
  is collected, prepared, processed, and shared vary a lot, ranging from hi
 ghly centralized to small-scale and perhaps siloed approaches. We propose 
 this minisymposium as a forum for experts to share and discuss practices, 
 requirements and challenges for data management. Our aim is to highlight b
 est practices and challenges across the breadth of scientific computing an
 d establish a common understanding of scientific data management, to enabl
 e the co-design of HPC methods and tools for a diverse portfolio of data-d
 riven, cross-domain scientific workflows and research infrastructure. This
  minisymposium will consist of three 25-minute presentations, followed by 
 a Q&A session organized as a panel with the presenters. For all our invite
 d speakers, we gave the same assignment: introduce your research and ideas
  through the lens of demand and offer for next generation data management 
 for scientific computing. This will be the focus of the panel, as well.\n\
 nPanel Session\n\nThis session will be dedicated to a panel discussion and
  Q&A of the presenters of this minisymposium. Our aim is to identify and d
 iscuss common practices and challenges, and share ideas on how to overcome
  these challenges and ultimately contribute to HPC systems co-design.\n\n\
 nVincenzo Eduardo Padulano (CERN)\n---------------------\nData Storage and
  Management for High-Energy Physics\n\nBetween the four main experiments a
 t CERN’s Large Hadron Collider, an estimated 2 EB of high-energy-physics (
 HEP) data has been recorded and stored since 2009.\nEach experiment is gov
 erned by a large collaboration, consisting of over one hundred institutes 
 from all over the world. The scale of ...\n\n\nFlorine Willemijn de Geus (
 CERN, University of Twente)\n---------------------\nKVCache Data Managemen
 t in Distributed GenAI Workloads: Characteristics, Requirements, and Chall
 enges\n\nGenerative AI (GenAI) workloads — code completion, intelligent ag
 ents, semantic RAG, and recommender systems — have become widely adopted a
 cross many domain-specialized settings. These systems run on large-scale, 
 high-performance infrastructure of thousands of GPUs, storage, and network
 i...\n\n\nAnimesh Trivedi (IBM Research)\n---------------------\nRDM vs Sc
 ience-First Thinking About Data Management\n\nResearch Data Management (RD
 M) is crucial for ensuring research integrity, reproducibility, and reuse,
  while maximizing the impact and visibility of scientific findings. As suc
 h, RDM and the FAIR principles are pillars of open science. In the last de
 cade, RDM has been widely implemented, supported b...\n\n\nRob van Nieuwpo
 ort (Leiden University)\n\nDomain: Climate, Weather, and Earth Sciences, L
 ife Sciences, Physics, Computational Methods and Applied Mathematics\n\nSe
 ssion Chair: Vincenzo Eduardo Padulano (CERN)
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