CITATION.cff

cff-version: 1.2.0
title: "Sea ice forecasting using the IceNet library (Jupyter Notebook) published in the Environmental Data Science book"
message: "If you use this software, please cite it as below."
type: software
authors:
  - given-names: James
    family-names: Byrne
    email: jambyr@bas.ac.uk
    affiliation: British Antarctic Survey
    orcid: "https://orcid.org/0000-0003-3731-2377"
  - given-names: Bryn Noel
    family-names: Ubald
    email: bryald@bas.ac.uk
    affiliation: British Antarctic Survey
    orcid: "https://orcid.org/0000-0002-0206-7140"
  - name: "This EDS book notebook contributors"
    website: "https://github.com/eds-book-gallery/67a1e320-7c47-4ea9-8df8-e868326bc90b/graphs/contributors"
version: v1.0.1
identifiers:
  - type: doi
    value: 10.5281/zenodo.12613298
    description: The concept DOI for the collection containing all versions of the notebook.
  - type: doi
    value: 10.1038/s41467-021-25257-4
    description: Original paper on IceNet
repository-code: >-
  https://github.com/eds-book-gallery/67a1e320-7c47-4ea9-8df8-e868326bc90b
url: "https://icenet.ai/"
repository: "https://github.com/icenet-ai/icenet"
abstract: >-
  IceNet is a python library that provides the ability to
  download, process, train and predict from end to end.
  Users can interact with IceNet either via the python
  interface or via a set of command-line interfaces (CLI)
  which provide a high-level interface that covers the above
  abilities.


  This project demonstrates the use of the python library
  API for forecasting sea ice for a reduced dataset to
  demonstrate the end-to-end capabilities of IceNet, from
  source data download to forecast generation.
keywords:
  - sea-ice
  - forecast
  - cryosphere
  - antarctic
  - arctic
  - ice
  - machine learning
  - unet
  - deep learning
license: MIT