CITATION.cff

cff-version: 1.2.0
title: "Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (Jupyter Notebook) published in the Environmental Data Science book"
message: "If you use this software, please cite it using the metadata from this file."
type: software
authors:
  - family-names: Malhotra
    given-names: Garima
    orcid: https://orcid.org/0000-0003-4179-628X
    website: https://github.com/garimamalhotra
    affiliation: University of Colorado Boulder
  - family-names: Pinto Veizaga
    given-names: Daniela
    orcid: https://orcid.org/0009-0005-8588-3774
    website: https://github.com/dapivei
    affiliation: University of California Berkeley
  - family-names: Peña Velasco
    given-names: Jorge Eduardo
    website: https://github.com/jedpe
    affiliation: Claremont McKenna College
  - name: "This EDS book notebook contributors"
    website: "https://github.com/eds-book-gallery/3286b92f-4fae-4cc6-a29e-e408bc844542/graphs/contributors"
version: v1.0.19 # This is automatically set using the bumpversion tool.
identifiers:
  - type: doi
    value: 10.5281/zenodo.8296922
    description: The concept DOI for the collection containing all versions of the notebook.
abstract: "Notebook developed to demonstrate the computational reproduction of the paper A Sensitivity Analysis of a Regression Model of Ocean Temperature, published in Environmental Data Science journal."
references:
  - authors:
      - family-names: Furner
        given-names: Rachel
      - family-names: Haynes
        given-names: Peter
      - family-names: Munday
        given-names: Dave
      - family-names: Brooks
        given-names: Paige
      - family-names: Jones
        given-names: Daniel C.
      - family-names: Shuckburgh
        given-names: Emily
    doi: 10.1017/eds.2022.10
    type: article
    scope: "Reproduced paper as part of the 2023 Climate Informatics Reproducibility Challenge."
    title: "A Sensitivity Analysis of a Regression Model of Ocean Temperature"
    journal: "Environmental Data Science journal"
    year: 2022
keywords:
  - Environmental Data Science
  - Oceanography
  - Modelling
  - Reproducibility Challenge