Dependency Management: Difference between revisions

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[[Category:Beginner's_Guide_Reproducibility|02]]
[[Category:Beginner's_Guide_Reproducibility|02]]
{{ReproducibilitySidebar}}


'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.
'''Dependencies''' are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and '''which versions of each dependency''' are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.

Revision as of 20:25, 28 June 2026


Reproducibility & Collaboration Checklist
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Dependencies are any software or software libraries that your research workflow requires to execute. For example, data analysis in Python often relies on the NumPy or Pandas Python packages, while R workflows might rely in the Tidyverse library. It is important to keep track of which dependencies and which versions of each dependency are being used in your workflow. Unclear or insufficient dependency management can lead to inconsistent results or failure of other researchers to reproduce your results.

Procedures and Tools

Title Description Technical Level Relevance
Virtual Environment Introduction Introduces virtual environments for dependency management
need to organize