This is best viewed in Pangeo Jupyter Lab

1. Create a history

   Create history

   1. Make sure you start from an empty analysis history.
   2. Rename your history to be meaningful and easy to find. For instance, you can choose Xarray with Pangeo notebook as the name of your new history.

2. Upload CAMS PM2.5 data

   Data can be retrieved directly from Copernicus Atmosphere Monitoring Service but to make it easier, you can download the tutorial data from Zenodo.

   Data upload

   1. Create a new history for this tutorial

   2. Import the files from Zenodo or fromthe shared data library (GTN - Material -> Climate -> Pangeo Notebook in Galaxy - Introduction to Xarray):

      https://zenodo.org/record/5805953/files/CAMS-PM2_5-20211222.netcdf
      

   3. Rename the dataset to CAMS-PM2_5-20211222.netcdf
   4. Check that the datatype is netcdf

3. Starting Galaxy Pangeo JupyterLab

   Launch Pangeo notebook JupyterLab in Galaxy

   Currently Pangeo notebook in Galaxy is available on useGalaxy.eu only. Make sure you login to Galaxy and search for Pangeo notebook and not the default JupyterLab in Galaxy to make sure you ahve all the Pangeo Software stack available. The default JupyterLab in Galaxy would not be sufficient for executing all the tasks in this tutorial.

   1. Open the Pangeo Notebook Tool: interactive_tool_pangeo_notebook by clicking here
   2. Include data into the environment: CAMS-PM2_5-20211222.netcdf
   3. Click on Execute: the tool will start running and will stay running
   4. Click on the User menu at the top and go to Active Interactive Tools and locate the Pangeo JupyterLab instance you started.
   5. Click on your Pangeo JupyterLab instance

4. Once the notebook has started, open a Terminal in Jupyter File → New → Terminal

5. Run the following command:

   wget https://training.galaxyproject.org/training-material/topics/climate/tutorials/pangeo-notebook/climate-pangeo-notebook.ipynb
   

6. Switch to the Notebook that appears in the list of files on the left

Introduction

In this tutorial, we will learn about Xarray, one of the most used Python library from the Pangeo ecosystem.

We will be using data from Copernicus Atmosphere Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. Parallel data analysis with Pangeo is not covered in this tutorial.

Comment: Remark

This tutorial uses data on a regular latitude-longitude grid. More complex and irregular grids are not discussed in this tutorial. In addition, this tutorial is not meant to cover all the different possibilities offered by Xarrays but shows functionalities we find useful for day to day analysis.

Agenda

In this tutorial, we will cover:

1. Introduction
2. Analysis
   1. Import Python packages
   2. Open and read metadata
   3. Select / Subset from coordinates
   4. Plotting
   5. How to use the where method
   6. Reduction operations
   7. Details on the resample method
3. Conclusion

Analysis

Import Python packages

Some packages may need to be installed first. For example cmcrameri is missing, so we need to install it by entering the following command in a new cell of your Jupyter Notebook:

pip install cmcrameri

Then we need to import all the necessary packages in our Jupyter Notebook.

import numpy as np
import xarray as xr
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cmcrameri.cm as cmc
import pandas as pd

Open and read metadata

The netCDF dataset can now be opened with Xarray:

dset = xr.open_dataset("data/CAMS-PM2_5-20211222.netcdf")

Once opened, we can get metadata using print statement.

print(dset)

Below is what you should get if everything goes fine.

Output

 <xarray.Dataset>
 Dimensions:     (longitude: 700, latitude: 400, level: 1, time: 97)
 Coordinates:
   * longitude   (longitude) float32 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
   * latitude    (latitude) float32 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
   * level       (level) float32 0.0
   * time        (time) timedelta64[ns] 00:00:00 01:00:00 ... 4 days 00:00:00
Data variables:
  pm2p5_conc  (time, level, latitude, longitude) float32 0.4202 ... 7.501
Attributes:
  title:        PM25 Air Pollutant FORECAST at the Surface
  institution:  Data produced by Meteo France
  source:       Data from ENSEMBLE model
  history:      Model ENSEMBLE FORECAST
  FORECAST:     Europe, 20211222+[0H_96H]
  summary:      ENSEMBLE model hourly FORECAST of PM25 concentration at the...
  project:      MACC-RAQ (http://macc-raq.gmes-atmosphere.eu)

Tip: Command not found

If you get an error with the previous command, first check the location of the input file CAMS-PM2_5-20211222.netcdf: it needs to be in the same directory as your Jupyter Notebook.

We can identify 4 different sections:

1. Dimensions: name of dimensions and corresponding number of elements;
2. Coordinates: contains coordinate arrays (longitude, latitude, level and time) with their values.
3. Data variables: contains all the variables available in the dataset. Here, we only have one variable. For each variable, we get information on its shape and values.
4. Attributes: at this level, we get all the attributes of the dataset.

We can also get metadata information for each coordinate and data variables using “.” followed by the coordinate or data variable name.

print(dset.time)

Question: CAM PM2.5 Dataset

What is the name of the variable for Particle matter < 2.5 μm and its physical units?

Solution

1. To get metadata information from pm2p5_conc Data variable, we use its variable name and print it. Printing it will only print metadata, not the values.
   • Variable name: mass_concentration_of_pm2p5_ambient_aerosol_in_air
   • Units: µg/m3

Input: Python

print(dset.pm2p5_conc)

Output

   <xarray.DataArray 'pm2p5_conc' (time: 97, level: 1, latitude: 400, longitude: 700)>
   [27160000 values with dtype=float32]
   Coordinates:
     * longitude  (longitude) float32 335.0 335.1 335.2 335.4 ... 44.75 44.85 44.95
     * latitude   (latitude) float32 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
     * level      (level) float32 0.0
     * time       (time) timedelta64[ns] 00:00:00 01:00:00 ... 4 days 00:00:00
   Attributes:
       species:        PM2.5 Aerosol
       units:          µg/m3
       value:          hourly values
       standard_name:  mass_concentration_of_pm2p5_ambient_aerosol_in_air

Comment: Different ways to access Data variables

To access a variable or coordinate, we can use “.” or specify its name as a string between squared brackets “[” “]”. For example:

print(dset['pm2p5_conc'])
# or alternatively
print(dset.pm2p5_conc)

When we print a variable or coordinate, we do not get all the individual values but a DataArray that contains a lot of very useful metadata such as coordinates (if they have some), all the attributes such as the name, the physical units, etc.

Select / Subset from coordinates

We often want to select elements from the coordinates for instance to subset a geographical area or select specific times or a specific time range.

There are two different ways to make a selection:

• by index
• by value

Select elements from coordinate by index

print(dset.isel(time=0))

You should see that the coordinate time “disappeared” from the Dimensions and now the variable pm2p5_conc is a 3D field with longitude, latitude and level.

Select elements from coordinates by value

When selecting elements by the value of the coordinate, we need to use the same datatype. For instance, to select an element from time, we need to use timedelta64. The code below will give the same result as isel(time=0).

print(dset.sel(time=np.timedelta64(0)))

The output will be very similar to what we did previously when selecting from coordinates by index.

Question: Select a single time for PM2.5

How to select the forecast for December, 24th 2021 at 12:00 UTC?

Solution

Data starts on December, 22nd 2021 at 00:00 UTC so we need to add 2 days and 12 hours to select the correct time index.

Input: Python

print(dset.sel(time=(np.timedelta64(2,'D')+ np.timedelta64(12,'h'))))

Output

<xarray.Dataset>
Dimensions:     (longitude: 700, latitude: 400, level: 1)
Coordinates:
  * longitude   (longitude) float32 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
  * latitude    (latitude) float32 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * level       (level) float32 0.0
    time        timedelta64[ns] 2 days 12:00:00
Data variables:
    pm2p5_conc  (level, latitude, longitude) float32 0.4499 0.4421 ... 10.71
Attributes:
    title:        PM25 Air Pollutant FORECAST at the Surface
    institution:  Data produced by Meteo France
    source:       Data from ENSEMBLE model
    history:      Model ENSEMBLE FORECAST
    FORECAST:     Europe, 20211222+[0H_96H]
    summary:      ENSEMBLE model hourly FORECAST of PM25 concentration at the...
    project:      MACC-RAQ (http://macc-raq.gmes-atmosphere.eu)

Plotting

To plot a map, you need to select a variable with data on geographical coordinates (latitude, longitude). In addition, coordinates need to be sorted, and preferably in increasing order. This is not the case for the coordinate “longitude” which is given between 360 and 0.

Let’s shift the longitudes by 180 degrees so that they come in the range of -180 to 180.

Shift longitudes

We print the longitudes before and after shifting them so we can see what is happening.

print(dset.longitude)

The longitude values are between 335.05 and 44.95 degrees.

Let’s now shift the longitudes to get values between -180, 180 degrees.

dset.coords['longitude'] = (dset['longitude'] + 180) % 360 - 180
print(dset.longitude)

Indeed, the longitudes have been shifted and now the values are between -24.95 and 44.95.

Visualize on a map PM2.5 for December, 24th 2021 at 12:00 UTC

dset.sel(time=(np.timedelta64(2,'D')+ np.timedelta64(12,'h'))).pm2p5_conc.plot()

We will get a figure like the one below:

Comment: What about `level`

Note that in the previous plot, we did not need to select level because there is one value only. However, if we had more than one level, we would need to add a selection on the level before plotting

Customize your plot

There are many ways to customize your plots and we will only detail what we think is important for creating publication ready figures:

• Define the size of the figure
• Choose to project data on a different projection.
• Add coastline
• Set the min and max values for plotting
• Add a title, change colorbar title
• Save figure into png

fig = plt.figure(1, figsize=[15,10])

# We're using cartopy to project our data.
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=ccrs.Mercator())
ax.coastlines(resolution='10m')

# We need to project our data to the new projection and for this we use `transform`.
# we set the original data projection in transform (here PlateCarree)
dset.sel(time=(np.timedelta64(2,'D') + np.timedelta64(12,'h')))['pm2p5_conc'].plot(ax=ax,
                                                                                    transform=ccrs.PlateCarree(),
                                                                                    vmin = 0, vmax = 35,
                                                                                    cmap=cmc.roma_r)
# One way to customize your title
plt.title("Copernicus Atmosphere Monitoring Service PM2.5, 2 day forecasts\n 24th December 2021 at 12:00 UTC", fontsize=18)
plt.savefig("CAMS-PM2_5-fc-20211224.png")

And you should get the following plot:

Multi-plots

Now, we will plot several times on the same figure in different sub-plots; we will not plot all the times (too many) but the first 24 forecasted values.

Firstly, we need to create a list of times and convert it to pandas datetime in order to make it easier to format times when plotting:

list_times = np.datetime64('2021-12-22') + dset.time.sel(time=slice(np.timedelta64(0),np.timedelta64(1,'D')))
print(pd.to_datetime(list_times).strftime("%d %b %H:%S UTC"))

Secondly, we need to use the same plotting method as earlier, but we pass additional parameters:

• vmin = 0and vmax = 35 to set the minimum and maximum values when plotting (this is useful to highlight features in your plot)
• subplot_kws={"projection": proj_plot} to project data on a non-default projection. See cartopy projection for more information about projections.
• col='time' because we will plot several time;
• col_wrap=4 to have a maximum of 4 plots per row. If we have more times to plot, then the next figures will be on another row.
• robust=True and aspect=dset.dims["longitude"] / dset.dims["latitude"] are additional parameters to make each subplot with a “sensible” figsize.
• cmap=cmc.roma_r to select a non-default and color-blind friendly colormap (see scientific colormaps).

fig = plt.figure(1, figsize=[10,10])

# We're using cartopy to project our data.
# (see documentation on cartopy)
proj_plot = ccrs.Mercator()

# We need to project our data to the new projection and for this we use `transform`.
# we set the original data projection in transform (here PlateCarree)
p = dset.sel(time=slice(np.timedelta64(1,'h'),np.timedelta64(1,'D')))['pm2p5_conc'].plot(transform=ccrs.PlateCarree(),
                                                                                       vmin = 0, vmax = 35,
                                                                                       subplot_kws={"projection": proj_plot},
                                                                                       col='time', col_wrap=4,
                                                                                       robust=True,
                                                                                      aspect=dset.dims["longitude"] / dset.dims["latitude"],  # for a sensible figsize
                                                                                       cmap=cmc.roma_r)
# We have to set the map's options on our axes
for ax,i in zip(p.axes.flat,  (np.datetime64('2021-12-22') + dset.time.sel(time=slice(np.timedelta64(0),np.timedelta64(1,'D')))).values):
      ax.coastlines('10m')
      ax.set_title("CAMS PM2.5 " + pd.to_datetime(i).strftime("%d %b %H:%S UTC"), fontsize=12)
# Save your figure
plt.savefig("CAMS-PM2_5-fc-multi.png")

In the second part of our plot, we are going to customize each subplot (this is why we loop for each of them and get their axes) by adding:

• coastlines: we pass a parameter 10m to get coastlines with a high resolution (non-default);
• set_title to set a title for each subplot.

Question: PM2.5 over Italy

Using a Multi-plot between Rome and Naples, can you tell us if the forecasted PM2.5 will increase or decrease during the first 24 hours?

Solution

We will select a sub-area: 11. East to 15.0 East and 40. N to 43. N. PM2.5 will increase and reach values close to 35 μm.m-3. We will use slice to select the area and we slice latitudes with latitude=slice(47.3, 36.5) and not latitude=slice(36.5, 47.3). The reason is that when using slice, you need to specify values using the same order as in the coordinates. Latitudes are specified in decreasing order for CAMS.

Input: Python

fig = plt.figure(1, figsize=[10,10])

# We're using cartopy to project our data.
# (see documentation on cartopy)
proj_plot = ccrs.Mercator()

# We need to project our data to the new projection and for this we use `transform`.
# we set the original data projection in transform (here PlateCarree)
p = dset.sel(time=slice(np.timedelta64(1,'h'),np.timedelta64(1,'D'))).sel(latitude=slice(43., 40.),
                                                                          longitude=slice(11.,15.))['pm2p5_conc'].plot(transform=ccrs.PlateCarree(),
                                                                                     vmin = 0, vmax = 35,
                                                                                     subplot_kws={"projection": proj_plot},
                                                                                     col='time', col_wrap=4,
                                                                                     robust=True,
                                                                                     aspect=dset.dims["longitude"] / dset.dims["latitude"],  # for a sensible figsize
                                                                                     cmap=cmc.roma_r)
# We have to set the map's options on all axes
for ax,i in zip(p.axes.flat,  (np.datetime64('2021-12-22') + dset.time.sel(time=slice(np.timedelta64(0),np.timedelta64(1,'D')))).values):
    ax.coastlines('10m')
    ax.set_title("CAMS PM2.5 " + pd.to_datetime(i).strftime("%d %b %H:%S UTC"), fontsize=12)
# Save your figure
plt.savefig("CAMS-PM2_5-fc-multi-Italy.png")

How to use the where method

Sometimes we may want to make more complex selections with criteria on the values of a given variable and not only on its coordinates. For this purpose, we use the where method. For instance, we may want to only keep PM2.5 if values are greater than 25 μm.m-3 (or any threshold you would like to choose).

Mask values that do not meet a criteria with Where

print(dset.where(dset['pm2p5_conc'] > 25))

Comment: What happened?

Each element of the dataset where the criteria within the where statement is not met, e.g. when PM2.5 <= 25, will be set to nan. You may not see any changes when printing the dataset but if you look carefuly at pm2p5_conc values, you will see many nan.

Let’s plot one time to better see what happened:

######################
# Plotting with mask #
######################

fig = plt.figure(1, figsize=[15,10])

# We're using cartopy to project our data.
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=ccrs.Mercator())
ax.coastlines(resolution='10m')

# We need to project our data to the new projection and for this we use `transform`.
# we set the original data projection in transform (here PlateCarree)
dset.where(dset['pm2p5_conc'] > 25).isel(time=0)['pm2p5_conc'].plot(ax=ax,
                                                                     transform=ccrs.PlateCarree(),
                                                                     vmin = 0, vmax = 35,
                                                                     cmap=cmc.roma_r)
# One way to customize your title
plt.title("Copernicus Atmosphere Monitoring Service PM2.5, 2 day forecasts\n 24th December 2021 at 12:00 UTC\n only values > 25", fontsize=18)
plt.savefig("CAMS-PM2_5-fc-20211224-25.png")

We can then make the same multi-plot as earlier (over Italy) but with a where statement to mask values lower than 25 μm.m-3:

Multi-plot over Italy using a mask

fig = plt.figure(1, figsize=[10,10])

# We're using cartopy to project our data.
# (see documentation on cartopy)
proj_plot = ccrs.Mercator()

# We need to project our data to the new projection and for this we use `transform`.
# we set the original data projection in transform (here PlateCarree)
p = dset.where(dset['pm2p5_conc'] > 25).sel(time=slice(np.timedelta64(1,'h'),np.timedelta64(1,'D'))).sel(latitude=slice(43., 40.),
                                                                           longitude=slice(11.,15.))['pm2p5_conc'].plot(transform=ccrs.PlateCarree(),
                                                                                      vmin = 0, vmax = 35,
                                                                                     subplot_kws={"projection": proj_plot},
                                                                                     col='time', col_wrap=4,
                                                                                     robust=True,
                                                                                     aspect=dset.dims["longitude"] / dset.dims["latitude"],  # for a sensible figsize
                                                                                     cmap=cmc.roma_r)
# We have to set the map's options on all four axes
for ax,i in zip(p.axes.flat,  (np.datetime64('2021-12-22') + dset.time.sel(time=slice(np.timedelta64(0),np.timedelta64(1,'D')))).values):
     ax.coastlines('10m')
     ax.set_title("PM2.5 > 25 μm.m-3" + pd.to_datetime(i).strftime("%d %b %H:%S UTC"), fontsize=12)
# Save your figure
plt.savefig("CAMS-PM2_5-fc-multi-Italy-25.png")

Reduction operations

We often want to compute the mean of all our datasets, or along a dimension (for instance time). If you do not pass any argument to the operation then it is done over all dimensions.

Details on the mean method

When we do not specify any parameters, we get a single value.

print(dset.sel(latitude=slice(43., 40.), longitude=slice(11.,15.)).mean())

Output

<xarray.Dataset>
Dimensions:     ()
 Data variables:
    pm2p5_conc  float32 9.118

Question: Maximum PM2.5 over Italy

What is the maximum forecasted PM2.5 value over the Rome-Naples region?

Solution

We select the same sub-area: 11. East to 15.0 East and 40. N to 43. N and compute the maximum with max. The maximum PM2.5 value is 59.13694382 μm.m-3 (that is rounded to 59.14).

Input: Python

dset.sel(latitude=slice(43., 40.), longitude=slice(11.,15.)).max()

Output

xarray.Dataset
Dimensions:
Coordinates: (0)
Data variables:
pm2p5_conc
()
float64
59.14
array(59.13694382)
Attributes: (0)

Question: Find when the maximum PM2.5 is forecasted

When is the maximum PM2.5 value forecasted?

Solution

We will select a sub-area: 11. East to 15.0 East and 40. N to 43. N and average over the entire selected area and search where the maximum PM2.5 value of 59.13694382 μm.m-3 is found. The maximum PM2.5 value occurs on 2021-12-22 at 20:00 UTC.

Input: Python

dset_tmean = dset.sel(latitude=slice(43., 40.), longitude=slice(11.,15.)).max(dim=('latitude', 'longitude'))
dset_tmean_max = dset_tmean.where(dset_tmean['pm2p5_conc'] == 59.13694382, drop=True)
print(dset_tmean_max)

Output

<xarray.Dataset>
Dimensions:     (time: 1, level: 1)
Coordinates:
  * level       (level) float32 0.0
  * time        (time) timedelta64[ns] 20:00:00
Data variables:
    pm2p5_conc  (time, level) float32 59.14

Comment: Pixel size when averaging

We average over a relatively small area so we do not make a weighted average. Use weighted averages when averaging over the entire globe or over a large area where the pixel sizes may vary (depending on the latitude).

Details on the resample method

1 day Resampling

The resampling frequency is lower than our original data, so we would need to apply a global operation on the data we group together such as mean, min, max:

print(dset.resample(time='1D').mean())

Output

<xarray.Dataset>
Dimensions:     (time: 5, longitude: 700, latitude: 400, level: 1)
Coordinates:
  * time        (time) timedelta64[ns] 0 days 1 days 2 days 3 days 4 days
  * longitude   (longitude) float32 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
  * latitude    (latitude) float32 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * level       (level) float32 0.0
Data variables:
    pm2p5_conc  (time, level, latitude, longitude) float32 0.4298 ... 7.501

30 minute resampling

When the resampling frequency is higher than the original data, we need to indicate how to fill the gaps, for instance, interpolate and indicate which interpolation method to apply or select nearest values, etc.:

print(dset.resample(time='30min').interpolate('linear'))

Comment: Be careful when sub-sampling!

Increasing the frequency of your data e.g. artificially creating data may not be scientifically relevant. Please use it carefully! Interpolating is not always scientifically relevant and sometimes you may prefer to choose a different method, like taking the nearest value for instance:

Input: Python

 dset.resample(time='30min').nearest()

Question: PM2.5 over Italy in the next 4 days

Using a Multi-plot between Rome and Naples, and making averages per day, can you tell us if forecasted PM2.5 will increase or decrease?

Solution

PM2.5 over Italy is overall decreasing over the next 4 forecasted days.

Input: Python

fig = plt.figure(1, figsize=[10,10])

# We're using cartopy to project our data.
# (see documentation on cartopy)
proj_plot = ccrs.Mercator()

sub_dset = dset.sel(latitude=slice(43., 40.), longitude=slice(11.,15.)).resample(time='1D').mean()
# We need to project our data to the new projection and for this we use `transform`.
# we set the original data projection in transform (here PlateCarree)
p = sub_dset['pm2p5_conc'].plot(transform=ccrs.PlateCarree(),
                                vmin = 0, vmax = 35,
                                subplot_kws={"projection": proj_plot},
                                col='time', col_wrap=5,
                                robust=True,
                                aspect=dset.dims["longitude"] / dset.dims["latitude"],  # for a sensible figsize
                                cmap=cmc.roma_r)
# We have to set the map's options on all axes
for ax,i in zip(p.axes.flat,  (np.datetime64('2021-12-22') + dset.time.sel(time=slice(np.timedelta64(0),np.timedelta64(1,'D')))).values):
    ax.coastlines('10m')
    ax.set_title("CAMS PM2.5 " + pd.to_datetime(i).strftime("%d %b %H:%S UTC"), fontsize=12)
# Save your figure
plt.savefig("CAMS-PM2_5-fc-multi-Italy-mean-per-day.png")

Comment: `Grouby` versus `resample`

Use groupby instead of resample when you wish to group over a dimension that is not time. groupby is very similar to resample but can be applied to any coordinates and not only to time.

Conclusion

Well done! Pangeo is a fantastic community with many more resources for learning and/or contributing! Please, if you use any Python packages from the Pangeo ecosystem, do not forget to cite Pangeo Abernathey et al. 2017, Abernathey et al. 2021, Gentemann et al. 2021 and Sambasivan et al. 2021!

Have a look at the Pangeo Tutorial Gallery to pick up your next Pangeo training material!

Key points

• Pangeo ecosystem enables big data analysis in geosciences

• Xarray is an important Python package for big data analysis in geosciences

• Xarray can be used to read, select, mask and plot netCDF data

• Xarray can also be used to perform global operations such as mean, max, min or resampling data

Frequently Asked Questions

References

1. Abernathey, R., kevin paul, joe hamman, matthew rocklin, chiara lepore et al., 2017 Pangeo NSF Earthcube Proposal. 10.6084/m9.figshare.5361094.v1 https://figshare.com/articles/Pangeo_NSF_Earthcube_Proposal/5361094
2. Abernathey, R. P., T. Augspurger, A. Banihirwe, C. C. Blackmon-Luca, T. J. Crone et al., 2021 Cloud-Native Repositories for Big Scientific Data. Computing in Science & Engineering 23: 26–35. 10.1109/mcse.2021.3059437
3. Gentemann, C. L., C. Holdgraf, R. Abernathey, D. Crichton, J. Colliander et al., 2021 Science Storms the Cloud. 10.1002/essoar.10506344.2
4. Sambasivan, N., S. Kapania, H. Highfill, D. Akrong, P. Paritosh et al., 2021 “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI, in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, ACM. 10.1145/3411764.3445518

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Citing this Tutorial

1. Anne Fouilloux, 2022 Pangeo Notebook in Galaxy - Introduction to Xarray (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/climate/tutorials/pangeo-notebook/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{climate-pangeo-notebook,
author = "Anne Fouilloux",
title = "Pangeo Notebook in Galaxy - Introduction to Xarray (Galaxy Training Materials)",
year = "2022",
month = "10",
day = "18"
url = "\url{https://training.galaxyproject.org/training-material/topics/climate/tutorials/pangeo-notebook/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Batut_2018,
    doi = {10.1016/j.cels.2018.05.012},
    url = {https://doi.org/10.1016%2Fj.cels.2018.05.012},
    year = 2018,
    month = {jun},
    publisher = {Elsevier {BV}},
    volume = {6},
    number = {6},
    pages = {752--758.e1},
    author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning},
    title = {Community-Driven Data Analysis Training for Biology},
    journal = {Cell Systems}
}
                   

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