gin commit from PF3RD7D6

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README.md

@@ -14,6 +14,9 @@ Part 0 explains the usage of `xarray`, a Python package used to represent data i
 Information on the various stages of the workflow, and on the dataset details are located in the folder `tutorials/slides.html`. Each tutorial goes through a sequence of processing steps 
 on the example datasets, and ends on a practical exercise to explore the dataset further.
 
+Another set of tutorials can be found on the EBRAINS Collaboratory (requires free registration, allows online execution):
+https://lab.jsc.ebrains.eu/hub/login?next=%2Fhub%2Fapi%2Foauth2%2Fauthorize%3Fclient_id%3Djupyterhub-user-brovelli%26redirect_uri%3D%252Fuser%252Fbrovelli%252Foauth_callback%26response_type%3Dcode%26state%3DeyJ1dWlkIjogIjhjNWMxM2I2ZjgxOTRjOGU5ODhjMjYzYmEwNGE5NWZmIiwgIm5leHRfdXJsIjogIi91c2VyL2Jyb3ZlbGxpL2xhYi90cmVlL3NoYXJlZC9FQlJBSU5TJTIwQWNhZGVteSUyMFdvcmtzaG9wJTIwU2VyaWVzJTIwU2Vzc2lvbiUyMDElMjAlRTIlODAlOTMlMjBpbnRyYWNyYW5pYWwlMjBFRUcifQ
+
 
 ## Byron Yu: Dimensionality Reduction
 The notebook `tutorials/Exercise_PCA.ipynb` contains a the primary exercise centered around implementing a simple principle component analysis (PCA).
@@ -27,8 +30,6 @@ To get started, create a Python environment using the `environment.yml` file pro
 In addition, Byron Yu's graphical Matlab-based tool DataHigh and tutorials are available at https://users.ece.cmu.edu/~byronyu/software/DataHigh/datahigh.html and https://github.com/BenjoCowley/DataHigh
 
 
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 ## Martin Nawrot: Higher-order Correlations
 The notebook `tutorials/trial_by_trial_variability.ipynb` contains an exercise centered around time-resolved Fano Factors. The first lines of the exercises contain code that help you
 load the used datasets.