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Research Meeting 27423

Data in Context: Metadata Models for Quality Metrics and Utility Assessment

( Oct 17 – Oct 22, 2027 )
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Please use the following short url to reference this page: https://www.dagstuhl.de/27423
Organizers
  • Darren Bell (UK Data Service - Colchester, GB)
  • Michelle Edwards (University of Guelph, CA)
  • Arofan Gregory (DDI Alliance / CODATA - Jaffrey, New Hampshire, US)
  • Simon Hodson (CODATA - Paris, FR)
  • Hilde Orten (Sikt - Bergen, NO)
  • Joachim Wackerow (Ludwigshafen, DE)
Contact
Description
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This workshop focuses on generating more complete machine-actionable descriptions of data on which computational platforms are increasingly dependent. Modern technology – especially generative AI – consumes, produces, and exchanges data at an unprecedented scale. This places a premium on the technical and structural metadata for processing data as well as the semantic information needed to understand it, but further demands that we know the origin and quality of the data. One clear example is Large Language Models (LLMs), where we see problems of bias stemming from the nature of the training data. In the scientific world, there are computational approaches to reproducibility and replication of experiments, but a lack of standard models for describing the data on which they are based. The workshop will provide a foundation for the development of generalized solutions for both data consumers and producers.

Copyright Darren Bell, Michelle Edwards, Arofan Gregory, Simon Hodson, Hilde Orten, and Joachim Wackerow
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Seminar 27423
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