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A simple kit to use computational notebooks for more openness, reproducibility, and productivity in research
15 references·4 citing·44 second hop
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References

  • 2016Jupyter Notebooks – a publishing format for reproducible computational workflows3,069 cited
  • 2014Best Practices for Scientific Computing715 cited
  • 2017Good enough practices in scientific computing.452 cited
  • 2014Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE313 cited
  • 2018Open science, reproducibility, and transparency in ecology224 cited
  • 2016A Quick Introduction to Version Control with Git and GitHub194 cited
  • 2017Packaging Data Analytical Work Reproducibly Using R (and Friends)108 cited
  • 2009A Quick Guide to Organizing Computational Biology Projects95 cited
  • 2020Keeping modelling notebooks with TRACE: Good for you and good for environmental research and management support65 cited
  • 2021Reproducible Research: A Retrospective65 cited
  • 2018The drake R package: a pipeline toolkit for reproducibility and high-performance computing62 cited
  • 2016Towards a more reproducible ecology49 cited
  • 2017Packaging data analytical work reproducibly using R (and friends)47 cited
  • 2021Dealing with software complexity in individual‐based models33 cited
  • 2015Best publishing practices to improve user confidence in scientific software

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24 cited

Cited by

  • 2022Sharing the Recipe: Reproducibility and Replicability in Research Across Disciplines14 cited
  • 2024A how-to guide for code sharing in biology9 cited
  • 2024Detailed Implementation of a Reproducible Machine Learning-Enabled Workflow8 cited
  • 2025Is archaeology a science? Insights and imperatives from 10,000 articles and a year of reproducibility reviews8 cited