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DTSTAMP:20260421T090514Z
LOCATION:Bldg. 8 - Room B 101
DTSTART;TZID=Europe/Stockholm:20260629T160000
DTEND;TZID=Europe/Stockholm:20260629T164000
UID:submissions.pasc-conference.org_PASC26_sess126_msa229@linklings.com
SUMMARY:RDM vs science-first thinking about data management
DESCRIPTION:Rob van Nieuwpoort (Leiden University)\n\nResearch Data Manage
 ment (RDM) is crucial for ensuring research integrity, reproducibility, an
 d reuse, while maximizing the impact and visibility of scientific findings
 . As such, RDM and the FAIR principles are pillars of open science. In the
  last decade, RDM has been widely implemented, supported by better tools, 
 data stewards, and institutional policies. The promise is that good RDM pr
 actices ultimately save time, enhance data quality, and boost reuse. \nNev
 ertheless, there still are challenges and limitations, such as cultural re
 sistance, the limited effectiveness of DMPs and a lack of resources. For m
 any researchers, it is not immediately clear that the promised benefits ar
 e actually happening in practice. One could argue that a policy-first appr
 oach to RDM is getting into the way of science, and a more pragmatic scien
 ce-first approach to data management for scientific workflows is needed.\n
 Using several concrete examples from radio astronomy, computer science and
  climatology, we will reflect on this theme: What are the current practice
 s? Do we have convincing RDM impact stories? Are there counter examples? D
 oes a disruptive data explosion lead to fundamental changes on how we deal
  with data? What is the impact of new technologies like AI foundation mode
 ls? What are metrics of success?\n\nDomain: Climate, Weather, and Earth Sc
 iences, Life Sciences, Physics, Computational Methods and Applied Mathemat
 ics\n\nSession Chairs: Florine Willemijn de Geus (CERN, University of Twen
 te); Vincenzo Eduardo Padulano (CERN); and Ana-Lucia Varbanescu (Universit
 y of Twente, University of Amsterdam)\n\n
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