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24 changes: 24 additions & 0 deletions Training/index.md
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Videos of presentations given at the [2022 CF training workshop][2022] are available at the [CF conventions YouTube channel][YouTube]


### Additional Training Resources

Please note that these resources were not developed as part of the CF project, and their content may not fully align with CF's best practices.


These materials, developed by **Luke Marsden** (Norwegian Meteorological Institute), provide valuable guidance on working with CF-NetCDF files. They are hosted on his YouTube channel, [LukeDataManager](https://www.youtube.com/@LukeDataManager).

- **Working with CF-NetCDF in Python**
- Written document: [NetCDF in Python: From Beginner to Pro](https://nordatanet.github.io/NetCDF_in_Python_from_beginner_to_pro)
- YouTube playlist:
<br>
<iframe width="400" height="225" src="https://www.youtube.com/embed/videoseries?list=PLpykv05eUR0rAuaLnybkZORnCwyND4CV4" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

- **Working with CF-NetCDF in R**
- Written document: [NetCDF in R: From Beginner to Pro](https://nordatanet.github.io/NetCDF_in_R_from_beginner_to_pro)
- YouTube playlist:
<br>
<iframe width="400" height="225" src="https://www.youtube.com/embed/videoseries?list=PLpykv05eUR0oqBYFNqtiRgJGYR2tpCYtw" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

- **Introduction to CF-NetCDF**
- A short video describing the classic data format, including CF and ACDD:
<br>
<iframe width="400" height="225" src="https://www.youtube.com/embed/FGHJhAFf1W0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

[2022]: 2022-Training-Workshop.md
[YouTube]: https://www.youtube.com/channel/UCKLq7PCVonFJA0ec98SMFZA
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title: CF Conventions Home Page
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# CF Metadata Conventions

The CF metadata conventions are designed to promote the processing and sharing of files created with the [NetCDF API][api].
The conventions define metadata that provide a definitive description of what the data in each variable represents, and the spatial and temporal properties of the data.
The CF metadata conventions are designed to promote the processing and sharing of files created with the [NetCDF API][api].
The conventions define metadata that provide a definitive description of what the data in each variable represents, and the spatial and temporal properties of the data.
This enables users of data from different sources to decide which quantities are comparable, and facilitates building applications with powerful extraction, regridding, and display capabilities.
The CF convention includes a standard name table, which defines strings that identify physical quantities.

Expand All @@ -24,7 +26,7 @@ The CF convention includes a standard name table, which defines strings that ide
CF is developed through open discussion on GitHub.
If you would like to propose a change, make a suggestion, report a problem or ask a question, please [see here][discussion].
Changes are decided according to the CF [governance arrangements][governance].
The CF community embraces a philosophy of producing excellence by maintaining an open and welcoming culture and an environment that promotes debate and inquiry in a respectful, bold and intellectually rigorous fashion.
The CF community embraces a philosophy of producing excellence by maintaining an open and welcoming culture and an environment that promotes debate and inquiry in a respectful, bold and intellectually rigorous fashion.

Initially CF was developed for gridded data from climate and forecast models (hence "CF") of the atmosphere and ocean, but its use has subsequently extended to other geosciences, and to observations as well as numerical models.
The use of CF is recommended where applicable by Unidata.
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NetCDF data can be processed as tables, points, rasters, and, most recently, [discrete sampling geometries](https://pro.arcgis.com/en/pro-app/latest/tool-reference/multidimension/how-netcdf-discrete-sampling-geometry-dsg-tools-work.htm).
In addition, various ArcGIS Pro tools enable multidimensional management (aggregation, subsetting, anomaly detection), [spatio-temporal analysis](https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/an-overview-of-the-space-time-pattern-mining-toolbox.htm) (trend detection, time-series clustering, changepoint detection, hot spot detection), and visualization as 3D features or [voxels](https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/what-is-a-voxel-layer-.htm).
Analysis results based on netCDF data can be shared and viewed across the web as interactive maps, 3D features, and voxels.

## Other tools

### Nansen Legacy template generator

This [template generator](https://www.nordatanet.no/aen/template-generator/config%3DCF-NetCDF) can be used to create spreadsheet templates that include column headers from the CF standard names. The templates include separate sheets for recording variable and global attributes, based on the CF and the Attribute Convention for Data Discovery (ACDD).
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