Dependency Management: Difference between revisions
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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. | '''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. | ||
Back to [[Beginner's Guide to Computation in Statistical Research]] | |||
== Procedures and Tools == | == Procedures and Tools == | ||
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|[[Virtual Environment Introduction]] | |||
|Introduces virtual environments for dependency management | |||
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*[https://info.stat.cmu.edu/index.php%3Ftitle=Introduction_to_containers Introduction to containers] | |||
*[https://info.stat.cmu.edu/index.php%3Ftitle=Docker,_and_why-you-should-use-it-for-your-research Docker, and why-you-should-use-it-for-your-research] | |||
*[https://info.stat.cmu.edu/index.php%3Ftitle=Tutorials Basic tutorial] | |||
*[https://info.stat.cmu.edu/index.php%3Ftitle=Running_Docker_locally_and_on_Hydras Running Docker locally and on Hydras] | |||
|need to organize | |||
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Revision as of 07:51, 10 April 2026
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.
Back to Beginner's Guide to Computation in Statistical Research
Procedures and Tools
| Title | Description | Technical Level | Relevance |
|---|---|---|---|
| Virtual Environment Introduction | Introduces virtual environments for dependency management | ||
| need to organize | |||
