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small fixes to API reference
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docs/getting_started/tutorial_quickstart.md

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After setting up the input data and particle start locations and times, we need to specify what calculations to do with
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the particles. These calculations, or numerical integrations, will be performed by what we call a {py:obj}`parcels.Kernel`, operating on
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all particles in the `ParticleSet`. The most common calculation is the advection of particles through the velocity field.
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Parcels comes with a number of common {py:obj}`parcels.kernels`, from which we will use the Runge-Kutta advection kernel `AdvectionRK2`:
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Parcels comes with a number of common {py:obj}`parcels.kernels`, from which we will use the Runge-Kutta advection kernel {py:obj}`parcels.kernels.AdvectionRK2`:
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```{code-cell}
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kernels = [parcels.kernels.AdvectionRK2]

src/parcels/_datasets/__init__.py

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Developers, note that you should only add functions that create idealised datasets to this subpackage if they are (a) quick to generate, and (b) only use dependencies already shipped with Parcels. No data files should be added to this subpackage. Real world data files should be added to the `Parcels-code/parcels-data` repository on GitHub.
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Parcels Dataset Philosophy
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**Parcels Dataset Philosophy**
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When adding datasets, there may be a tension between wanting to add a specific dataset or wanting to add machinery to generate completely parameterised datasets (e.g., with different grid resolutions, with different ranges, with different datetimes etc.). There are trade-offs to both approaches:
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Sometimes we may want to test Parcels against a whole range of datasets varying in a certain way - to ensure Parcels works as expected. For these, we should add machinery to create generated datasets.
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Structure
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--------
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**Structure**
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This subpackage is broken down into structured and unstructured parts. Each of these have common submodules:
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