Update userdocumentation.md note for slim derived data

docs/hyperloop/userdocumentation.md

@@ -7,7 +7,7 @@
 
 When opening a page in Hyperloop which has not been visited before, a guided tour will explain key concepts. These tours provide an interactive learning experience for Hyperloop, easily activated with a single click. They are ideal for beginners and for refreshing knowledge.
 
 Where appropriate, when one tour ends, the next will begin to explain the next section of Hyperloop. Tours can be exited at any time. Once closed, they will not automatically begin on future page visits. 
 
 <div align="center">
 <img src="../images/JoyrideWelcome.png" width="35%">
@@ -85,7 +85,7 @@
 <div align="center">
 <img src="../images/wagonShortcuts.png" width="80%">
 </div>
 
 ## <a name="wagon-settings"></a> Wagon Settings
 
 * <a name="wagonsettings"></a>In _Wagon settings_ you can modify the wagon name, work flow name, and select wagon's dependencies. The dependencies offered are wagons from the same _Analysis_ or from [_Service wagons_](#servicewagons).
@@ -93,7 +93,7 @@
 <div align="center">
 <img src="../images/wagonSettings.png" width="70%">
 </div>
 
 ## <a name="wagon-configuration"></a> Wagon Configuration
 
 * <a name="wagonconfiguration"></a>In _Configuration_ the wagon configuration corresponding to the workflow will be available in the _Base_. The configuration is divided per _Task_, hence if you need to add a new parameter, you will need add it in the following order: task, parameter and value.
@@ -120,26 +120,30 @@
 
 * In order to update the base and subwagon configuration with the latest version of the workflow, click on the button `↻ sync` in _Configuration_. By synchronizing the configuration, the parameters which no longer belong to the workflow will be removed, and the values of the wagon's _Base_ will be updated as well if they have not been modified by the user.
 
 ## <a name="wagon-derived-data"></a> Derived data 
 
 * <a name="wagonderived"></a>In _Derived Data_ the tables which are produced by the task are displayed. If activated, these are saved to the output if the train is run as a derived data production. The produced derived data can be made available by the operators and serve as input for subsequent trains. 
 
 ### <a name="deriveddatatypes"></a> Derived data types
 * At the moment, there are two types of derived data specifications:
   * Standard derived data (marked with 🗂️)- if the wagon is used in a train, this will produce derived data to be used for further analysis. The results will not be merged across runs and can be used as input for future train runs. Note that standard derived data trains do not submit automatically and may need additional approval. If in doubt, please seek advise before enabling derived data tables in your wagon configuration.
   * Slim derived data (marked with green bordered 🗂️) - similarly to the standard derived data case, if used in a train, this will produce derived data to be used for further analysis. This is reserved for derived data of small output size. The results will be merged across runs and are not available to use in future train runs. The data will be automatically deleted after a preset period of time. You can mark a wagon for running as slim derived data by checking `Ready for slim derived data`.
 
 * For wagons set as ready for slim derived data, two more fields need to be correctly set:
   * Max DF size - This sets the maximal dataframe size in the merging step. Has to be 0 for not-self contained derived data (which need parent file access).
   * Max derived file size - Sets the size limit for the output file size of the derived data file. This is an expert parameter which usually does not have to be changed. Only change this value if the processing in subsequent trains takes so long that the jobs fail. If set to 0 a good value will be automatically determined.
-
+
+```note
+When enabling `Ready for slim derived data` the option has to be selected for the main wagon and all dependencies which produce derived data.
+```
+
 * In order to update the derived data configuration with the latest version of the workflow, click on the button `↻ sync` in _Derived data_. By synchronizing the derived data, the tables which no longer belong to the workflow will be removed, and the values of the tables will be updated.
 
 <div align="center">
 <img src="../images/derivedDataEx.png" width="70%">
 </div>
 
 ## <a name="wagon-test-statistics"></a> Test Statistics 
 
 * <a name="wagonteststatistics"></a>_Test Statistics_ contains three graphs that display different metrics following the tests this wagon was part of. The first graph plots the _PSS Memory_ corresponding to each test run. The second one diplays the _CPU Time_, _Wall time_ and _Throughput_ along the test runs for this wagon. Finally, the third graph shows the _Output size_ at each test run.
 
@@ -288,7 +292,7 @@
 </div>
 
 * If you only want to see the top 10 graph with the highest average, check the Show top 10 largest box.
 
 * To produce this type of performance graphs for a local O2 execution, follow the instructions [here](#producing-performance-graphs-for-a-local-o2-execution).
 
 * Whenever a wagon configuration is changed, if there are enabled wagons (including wagons that depend on it), then the test is automatically reset and a new test is launched. However, if the enabled wagon was already composed in a train, the train will run with the wagons and dataset configuration of the time at which the train was created.
@@ -299,7 +303,7 @@
 
 1. [Adding a new wagon](#addwagon): You can create a wagon with your unmerged or unreleased workflow. If the workflow is not available, add manually the configuration of the wagon, and subwagons if needed. You can synchronize the wagon's configuration once the package tag that includes your pull request has been released.
 2. [Enabling a wagon in a dataset](#enabling-a-wagon): If you need to enable your wagon with workflow that is unmerged or unreleased, use a `Future tag based on pull request`. There is a list of the latest merged and unmerged pull requests available in the system, you can see the pull request number and description. Select the _pull request tag_ and enable the wagon in a dataset. By doing this, the wagon will be queued to test, and the test will begin once the _pull request_ has been merged to a package tag, and the package tag is released. And then, if the test is successful, it'll be composed in a train with the latest package tag available.
 
 ## <a name="warnings"></a> Warnings
 
 When a wagon test finishes in warning, this means that the wagon will not be included in the automatic composition schedule. Therefore, train composition can be requested in the Operation channel, where an operator will take care of the request. Before doing so, please review if you cannot fix the cause of the warning yourself. Depending on the nature of the warning and the degree of exceeding specific constraints, the operator will either compose your train or advise you to review and improve certain parts before requesting a train again. In the latter case, the user can analyze the test and review the logs, searching for ways of improving resource usage or other elements that caused the exceptions.