diff --git a/documentation/user_guide.html b/documentation/user_guide.html
index f8e9316..506fbcf 100644
--- a/documentation/user_guide.html
+++ b/documentation/user_guide.html
@@ -137,109 +137,109 @@
When you've filled in all the previous fields, you can click on the "Submit"
button. You'll be taken to a WYSIWYG form designed based on the template you've chosen. With
this type of form, you can immediately see how the date story will look once published.
-
+
Statistics Template
-
- The Statistics template is designed for a 'classic' type of data story,
- where
- you can combine textual information with statistical information such as counts and
- charts
- to create a narrative.
- In this particular case, the first information that can be filled in or changed are the
- colours, the title, the subtitle, the curator and an introductory description of the
- data
- story.
- With three different buttons you can add boxes with tests, sums and charts. Each box can
- be
- moved up and down or removed.
- Counts
-
- Counts are numbers that represent a sum of a certain type of data. They're
- used
- to give an overview, e.g. of the type of data that fills a record. This type of
- visualisation requires:
-
- - A SPARQL query, which should be designed to return a number with the variable name
- "count".
-
- - A short label to describe the number displayed.
-
- Charts
-
- Charts are the most important part of the data visualisation in this
- template.
- Three different types are provided:
-
- - Bar chart
-
- - Line chart
- - Doughnut chart
-
- For each chart, the following information must be provided:
-
- - Select the type (required).
-
- - Insert SPARQL query (required).
- - Insert chart Title (required).
- - Choose one or more operations (optional).
-
- SPARQL
- Query
-
- Overall, a query can be of two types:
-
- - Simple: means that the query retrieves non-aggregated data that's
- post-processed
- using operations.
-
- - Complex: means that the query retrieves information directly suitable for
- charting.
-
- However, the structure of the query also depends on the type of chart
- chosen, as
- different types of data are needed. In addition, depending on the optional operations
- chosen, there are also requirements for the variable names.
- In case of a simple query, bar chart, line chart and doughnut chart
- all
- require
- the count operation:
- a simple query provides information in the form of a list with the labels of the
- elements.
- In this case, the operation "count" must be applied. The required variable name is
- "label".
+
+
The Statistics template is designed for a 'classic' type of data story,
+ where
+ you can combine textual information with statistical information such as counts and
+ charts
+ to create a narrative.
+ In this particular case, the first information that can be filled in or changed are the
+ colours, the title, the subtitle, the curator and an introductory description of the
+ data
+ story.
+ With three different buttons you can add boxes with tests, sums and charts. Each box can
+ be
+ moved up and down or removed.
+
Counts
+
+
Counts are numbers that represent a sum of a certain type of data. They're
+ used
+ to give an overview, e.g. of the type of data that fills a record. This type of
+ visualisation requires:
+
+ - A SPARQL query, which should be designed to return a number with the variable name
+ "count".
+
+ - A short label to describe the number displayed.
+
+
Charts
+
+
Charts are the most important part of the data visualisation in this
+ template.
+ Three different types are provided:
+
+ - Bar chart
+
+ - Line chart
+ - Doughnut chart
+
+
For each chart, the following information must be provided:
+
+ - Select the type (required).
+
+ - Insert SPARQL query (required).
+ - Insert chart Title (required).
+ - Choose one or more operations (optional).
+
+
SPARQL
+ Query
+
+
Overall, a query can be of two types:
+
+ - Simple: means that the query retrieves non-aggregated data that's
+ post-processed
+ using operations.
+
+ - Complex: means that the query retrieves information directly suitable for
+ charting.
+
+
However, the structure of the query also depends on the type of chart
+ chosen, as
+ different types of data are needed. In addition, depending on the optional operations
+ chosen, there are also requirements for the variable names.
+
In case of a simple query, bar chart, line chart and doughnut chart
+ all
+ require
+ the count operation:
+ a simple query provides information in the form of a list with the labels of the
+ elements.
+ In this case, the operation "count" must be applied. The required variable name is
+ "label".
+ Example:
+ SELECT ?label WHERE { ?content ?type . ?type rdfs:label ?label . }
+
+
In case of a complex query, every chart requires its own
+ characteristics:
+
+
+ - For bar chart and doughnut chart, a complex query provides information about the
+ number
+ of repetitions ( number, variable "y") of certain elements (labels or numbers,
+ variable
+ "x").
+ Example:
+ SELECT ?label (COUNT(?entry) AS ?count) WHERE {?entry ?p . ?entry ?audience . ?audience rdfs:label ?label . } GROUP BY ?label
+
+ - For line chart, complex query returns information about the number of repetitions (
+ number, variable "y") of certain entities (labels or numbers, variable "x").
Example:
- SELECT ?label WHERE { ?content ?type . ?type rdfs:label ?label . }
-
- In case of a complex query, every chart requires its own
- characteristics:
-
-
- - For bar chart and doughnut chart, a complex query provides information about the
- number
- of repetitions ( number, variable "y") of certain elements (labels or numbers,
- variable
- "x").
- Example:
- SELECT ?label (COUNT(?entry) AS ?count) WHERE {?entry ?p . ?entry ?audience . ?audience rdfs:label ?label . } GROUP BY ?label
-
- - For line chart, complex query returns information about the number of repetitions (
- number, variable "y") of certain entities (labels or numbers, variable "x").
- Example:
- SELECT ?x (COUNT(?x) AS ?y) WHERE { { SELECT ?time (DAY(?time) AS ?x) WHERE { ?entry ?time . } } } GROUP BY ?x ORDER BY ?x
-
-
-
- Publish and Export
-
- Once all the information has been added, you can submit the form. This will
- create the final product, an HTML page containing the data history thus created, which
- can
- be accessed from the sidebar on the left. Each chart can be printed as a .png image or
- exported as embeddable code.
- It's possible to change the content of a data story at any time by clicking on the
- "modify"
- button in the sidebar.
-
+ SELECT ?x (COUNT(?x) AS ?y) WHERE { { SELECT ?time (DAY(?time) AS ?x) WHERE { ?entry ?time . } } } GROUP BY ?x ORDER BY ?x
+
+
+
+ Publish and Export
+
+
Once all the information has been added, you can submit the form. This will
+ create the final product, an HTML page containing the data history thus created, which
+ can
+ be accessed from the sidebar on the left. Each chart can be printed as a .png image or
+ exported as embeddable code.
+ It's possible to change the content of a data story at any time by clicking on the
+ "modify"
+ button in the sidebar.
+