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Marie59 authored Nov 29, 2023
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- netcdf data
- maps
- marine data
- temperature
- salinity
- climate
contributions:
authorship:
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>
{: .hands_on}


> <details-title> Some complementary information about your data </details-title>
> The data here are mediterranean Sea - Eutrophication and Acidity aggregated datasets
> EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity and contaminants. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on eutrophication and acidity, and covers the Mediterranean Sea. Data were aggregated and quality controlled by the 'Hellenic Centre for Marine Research, Hellenic National Oceanographic Data Centre (HCMR/HNODC)' in Greece.
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> ![Image in the middle pannel of the map](../../images/ocean_var/galaxy_output.png)
{: .hands_on}

# DIVAnd : Data-Interpolating Variational Analysis in n dimensions

## Change Datatype
> <hands-on-title>Change the datatype from ODV outputs</hands-on-title>
> Go on your out put 'data_from_Eutrophication_Med_profiles_2022_unrestricted'
>
> In the Datatypes section select **netcdf**
>
> {% snippet faqs/galaxy/datasets_change_datatype.md %}
>
{: .hands_on}

## Launch DIVAnd
Use ODV outputs (which you just changed the datatype) as DIVAnd input.
> <hands-on-title>Run DIVANnd</hands-on-title>
>
> 1. Use {% tool [DIVAnd](https://earth-system.usegalaxy.eu/root?tool_id=interactive_tool_divand) %} with the following parameters:
> - *"Do you already have a notebook"*: `Start with a fresh notebook`
> - *"Include data into the environment"*: `data_from_Eutrophication_Med_profiles_2022_unrestricted`
> 2. **Run tool**
> 3. {% snippet faqs/galaxy/interactive_tools_open.md tool="DIVAnd" %}
{: .hands_on}

Now that you are in yourjupyterlab with the right environment to use DIVAnd and a set of notebooks (in the folder **notebooks**) to guide you, you can start the rest of your analysis.
You can find your data from ODV in the **data** folder of the jupyterlab.

Once you are done you have to save all your wanted data and visualisation in the **outputs** folder and the go on the top left in the **file** section and click on **Exit**.

After a couple minutes your outputs shoul appear in your Galaxy history.


# Conclusion

Great you now know how to extract ocean's variables from an ODV collection.
Great you now know how to extract ocean's variables from an ODV collection and use these extracted data in DIVAnd.

# Extra information
Coming up soon follow ups tutorials on Coastal Water Dynamics workflow (and other Earth-System related trainings). Keep an {% icon galaxy-eye %} open if you are interested !

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