README.md

@@ -51,9 +51,6 @@ git remote add upstream https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge
 git pull upstream master
 ```
 
-### 2. Fill our [registration form](https://docs.google.com/forms/d/1KEnATjaLOtV-o4N8PLinPXYnpba7egKsCCH_efriCb4).
-
-Registrations are not required before October 31st 2021, but highly [appreciated for the flow of information](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge/-/issues/4).
 
 ### 3. Make the project private
 
@@ -72,18 +69,12 @@ description of your method.
 ### 4. Add the `s2saichallengescorer` user to your repo with Reporter permissions
 The scorer follows the code shown in the [verification notebook](https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/verification_RPSS.ipynb). The scorer's username on gitlab is `s2saichallengescorer`. You should add it to your project with `Reporter` permissions. Under "Members" - "Invite Members" - "GitLab member or Email address", add `s2saichallengescorer`. The scorer will only ever clone your repository and evaluate your submission. It will never make any changes to your code.
 
-### 5. Add the `s2s-ai-challenge` topic to your repository
-To add the project topic navigate to `Settings` -> `General` and then fill in the word `s2s-ai-challenge` in the 
-`Topics` field near the top of the page. If you have multiple topics you can separate them by commas.
+This is optional and not required for now :)
 
-This allows your repository to be recognized as a participant of the competition. Without
-this project topic or if you have not added the scorer as a member of your project 
-the automated scoring bot will not evaluate any of your submissions and none of your code
-or results will be considered for the competition.
 
 ## Make Predictions
 
-### 6. Start jupyter on renku or locally
+### 4. Start jupyter on renku or locally
 The simplest way to contribute is right from the Renku platform - 
 just click on the `Environments` tab in your renku project and start a new session.
 This will start an interactive environment right in your browser.
@@ -98,7 +89,7 @@ renku project URLs - use `renku clone` to clone the project on whichever machine
 Install [renku first with `pipx`](https://renku-python.readthedocs.io/en/latest/installation.html),
 and then `renku clone https://renkulab.io/gitlab/$YOURNAME/s2s-ai-challenge-$GROUPNAME.git`
 
-### 7. Train your Machine Learning model
+### 5. Train your Machine Learning model
 
 Get training data via 
 - [climetlab](https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge)
@@ -108,12 +99,12 @@ Get corresponding observations/ground truth:
 - [climetlab](https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge)
 - IRIDL: [temperature](http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.temperature/.daily/) and accumulated [precipitation](http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.UNIFIED_PRCP/.GAUGE_BASED/.GLOBAL/.v1p0/.extREALTIME/.rain)
 
-### 8. Let the Machine Learning model perform subseasonal 2020 predictions
+### 6. Let the Machine Learning model perform subseasonal 2020 predictions
 and save them as `netcdf` files.
 The submissions have to placed in the `submissions` folder with filename `ML_prediction_2020.nc`,
 see [example](https://renkulab.io/gitlab/aaron.spring/s2s-ai-competition-bootstrap/-/blob/master/submissions/ML_prediction_2020.nc).
 
-### 9. `git commit` training pipeline and netcdf submission
+### 7. `git commit` training pipeline and netcdf submission
 For later verification by the organizers, reproducibility and scoring of submissions,
 commit all code, input and output data. For the data files please use `git lfs`. 
 If you are unfamiliar with `git lfs` a short introduction can be found