Quickstart.md

@@ -151,7 +151,7 @@ Level 4 is performed by the end-user based on the validation reports and execute
 
 ## Validation report
 
-- The validation report is a standard Excel file; here we'll use [this](uploads/f98d98d8e409768bc7eb314f43bb91da/validatie.xlsx) as example (generated from the example file above)
+- The validation report is a standard Excel file; here we'll use [this](uploads/f7d9499f8320ca64581dc19c1b339c78/validatie.xlsx) as example (generated from the example file above)
 
 - The report contains 3 tabs
 
@@ -185,7 +185,7 @@ Level 4 is performed by the end-user based on the validation reports and execute
 
 <img src="uploads/e76f79200b55fb5e87822ecdaac42c0f/Screenshot_2022-08-22_at_10.49.38.png" width="60%" />
 
-- The validation shares the format of the one shown above for the level 2 validation; for reference, [here's](uploads/79845fafcb1f3560d40bf0c67184e3bd/validatie.xlsx) the validation report obtained using the test data for the personeelsamenstelling profile
+- The validation shares the format of the one shown above for the level 2 validation; for reference, [here's](uploads/7f64cc1116773baebf3c9118d92b9ed4/validatie.xlsx) the validation report obtained using the test data for the personeelsamenstelling profile
 
 - Validation errors at this level do not necessarily indicate that the data is incorrect but that it does not contain sufficient information to answer certain indicators.  This may suggest a issue with the data or that data simply does not apply for an indicator (for example, a time-limited contract mush have an end date but a permanent contract does not need that, so the absence from a permanent contract is not an error)
 
@@ -197,15 +197,15 @@ Level 4 is performed by the end-user based on the validation reports and execute
 
 - This tab lists all validation rules whether they are violated or not.  The list is roughly sorted by importance
 
-<img src="uploads/2c1b491d90192bc981555600eeefa8a5/Screenshot_2022-08-22_at_11.03.24.png" width="60%" />
+<img src="uploads/994972c9f23e056216edb69abbd001f6/Screenshot_2022-08-23_at_14.29.31.png" width="60%" />
 
 - The first column shows the severity of violation of the rule and the second the rule itself.  Column 3 is discussed below.  Column 4 indicates how often a rule was violated and column 5 how large a percentage of the time the rule was satisfied (100% = good, 0% = bad).  The final column indicates which part of the model will have issues due to the violated model (ontology or indicator).
 
 - Violations with a low satisfaction percentage are likely problems with the data.
 
-- Column 3 indicates how often the validation rule was triggered.  This allows finding vacuously satisfied rules.  In this example, the first 8 rules are vacuously satisfied: no errors are found but that's not due to data adhering to the rule, it is because the dataset contained no data that matches the rule in the first place (a rule talking about time-limited contracts will never trigger on a dataset with only permanent contracts)
+- Column 3 indicates how often the validation rule was triggered.  This allows finding vacuously satisfied rules.  In this example, the first 6 rules are vacuously satisfied: no errors are found but that's not due to data adhering to the rule, it is because the dataset contained no data that matches the rule in the first place (a rule talking about time-limited contracts will never trigger on a dataset with only permanent contracts)
 
-<img src="uploads/b9ee50dc225b1e820b3e329c8ec13d2d/Screenshot_2022-08-22_at_11.03.46.png" width="60%" />
+<img src="uploads/ec54772399a8af83fc0c4cb389f23c2e/Screenshot_2022-08-23_at_14.29.45.png" width="60%" />
 
 - Spuriously satisfied rules may or not be an issue with missing data: the data is not there, but depending on the organisation that may just be a reflection of reality