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Communication Dans Un Congrès Année : 2024

Improving the reproducibility and provenance of urban drainage data and models with RENKU, a platform for sustainable data science

Alfredo Chavarría
  • Fonction : Auteur
Simon Tait
Mathieu Lepot
  • Fonction : Auteur
Jean-Luc Bertrand-Krajewski
João Paulo Leitão
  • Fonction : Auteur
Jörg Rieckermann
  • Fonction : Auteur
  • PersonId : 1116955

Résumé

• Improve the application of FAIR data principles in use of urban drainage research data and models through the data and code sharing platform RENKU, which tracks the provenance of datasets and derived information. • Provide a reproducible approach in three use cases from i) sediment research, ii) sensor calibration and data validation using the Urban Drainage Monitoring Toolbox and iii) automated in-pipe defect classification using and open sewer asset data. • Our insights suggest that RENKU is no panacea, but could be a cornerstone to making our research reproducible in the urban drainage community, to sharing data and models and to track the provenance of derived data
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Dates et versions

hal-04625766 , version 1 (26-06-2024)

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  • HAL Id : hal-04625766 , version 1

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Alfredo Chavarría, Simon Tait, Mathieu Lepot, Jean-Luc Bertrand-Krajewski, João Paulo Leitão, et al.. Improving the reproducibility and provenance of urban drainage data and models with RENKU, a platform for sustainable data science. 16th International Conference on Urban Drainage, TU Delft, Jun 2024, Delft, Netherlands. ⟨hal-04625766⟩
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