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@ -13,7 +13,8 @@ The intended readership of this wiki is:
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simulations practices](Research-simulation)
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- Interns working on simulations or just using code in general. They will find
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helpful advice on how to manage code, data and workflows.
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- Anyone interested in code, workflow and/or data management.
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- Anyone interested in code, workflow and/or data management *(including
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experimentalists!)*
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> **Disclaimer**: this wiki is not meant as a tutorial for the software that is
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> referenced. The reader is expected to document themselves thoroughly whenever
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@ -1 +1,38 @@
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# Good simulation practices
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# Principles of code and data management
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This page lists **mandatory** rules to be followed when working with code and
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data.
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## Working with code
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The objectives of these rules is to ensure:
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1. Code developped in the lab is preserved.
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2. Code can be easily shared in the lab and outside.
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3. Code can be easily reused by other people *(this includes future you!)*.
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4. Simulations are reproducible.
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### Tenets
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1. Every simulation and post-processing code must be versionned in a
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[Git](https://git-scm.com/book/en/v2) (or similar) repository on
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[git.dalembert.umpc.fr](https://git.dalembert.upmc.fr) (or similar).
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[Commit](https://git-scm.com/book/en/v2/Git-Basics-Recording-Changes-to-the-Repository)
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messages must be meaningful.
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2. Every repository must have a `README` file explaining what is the purpose of
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the code, what are the dependencies, and how to run the code, what the code
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produces, how to run tests.
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3. Every library repository must contain tests. Running the tests should be done
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with a single command.
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4. Every library must have a documented API, i.e. each function of the API has a
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basic description of what it does, along a description of inputs and outputs.
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5. Every library must have usage examples.
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6. Code versions used in a publication must be saved on
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[SoftwareHeritage](https://archive.softwareheritage.org/save/) and the
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resulting SWHID cited in the publication.
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## Working with data
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The objective of these rules is to enure:
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1. Data produced in the lab is preserved.
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2. Data can be easily shared in the lab and outside.
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3. Data can be easily resued by other people *(this includes future you!)*.
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4. Data origin can be traced.
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