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Communication Dans Un Congrès Année : 2022

Reinterpretation and Long-Term Preservation of Data and Code

Stephen Bailey
  • Fonction : Auteur
K.S. Cranmer
  • Fonction : Auteur
Matthew Feickert
  • Fonction : Auteur
Rob Fine
  • Fonction : Auteur
Clemens Lange
  • Fonction : Auteur

Résumé

Careful preservation of experimental data, simulations, analysis products, and theoretical work maximizes their long-term scientific return on investment by enabling new analyses and reinterpretation of the results in the future. Key infrastructure and technical developments needed for some high-value science targets are not in scope for the operations program of the large experiments and are often not effectively funded. Increasingly, the science goals of our projects require contributions that span the boundaries between individual experiments and surveys, and between the theoretical and experimental communities. Furthermore, the computational requirements and technical sophistication of this work is increasing. As a result, it is imperative that the funding agencies create programs that can devote significant resources to these efforts outside of the context of the operations of individual major experiments, including smaller experiments and theory/simulation work. In this Snowmass 2021 Computational Frontier topical group report (CompF7: Reinterpretation and long-term preservation of data and code), we summarize the current state of the field and make recommendations for the future.

Dates et versions

hal-03793023 , version 1 (30-09-2022)

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Citer

Stephen Bailey, K.S. Cranmer, Matthew Feickert, Rob Fine, Sabine Kraml, et al.. Reinterpretation and Long-Term Preservation of Data and Code. Snowmass 2021, Jul 2022, Seattle, United States. ⟨hal-03793023⟩
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