Further clarification for distributions

dcat/index.html

@@ -1725,7 +1725,7 @@ <h3>Class: Distribution</h3>
         <div class="note">
             <p> Examples of distributions include a CSV file, a netCDF file, a JSON document, or a data-cube, files made accessible according to different profiles, such as XML or JSON schemas or ShEx or SHACL expressions.</p>
 
-            <p>In some cases all distributions of a dataset will be fully informationally equivalent, in the sense that lossless transformations between the representations are possible. An example would be different serializations of an RDF graph using RDF/XML, Turtle, N3, JSON-LD. However, in other cases the distributions might have different levels of fidelity to the underlying data. For example, a graphical representation alongside a CSV file. The question of whether different representations can be understood to be distributions of the same dataset is use-case specific, so the judgement is the responsibility of the provider. </p>
+            <p>In some cases all distributions of a dataset will be fully informationally equivalent, in the sense that lossless transformations between the representations are possible. An example would be different serializations of an RDF graph using RDF/XML, Turtle, N3, JSON-LD. However, in other cases the distributions might have different levels of fidelity to the underlying data. For example, a graphical representation about the data on a CSV file may not contain exactly the same information recorded in the CSV file, but could be considered as two distributions for the same dataset as they are about the same data. The question of whether different representations can be understood to be distributions (or specific representations) of the same dataset is use-case specific, so the judgement is the responsibility of the provider. </p>
 
             <p>
                 The scope of <code>dcat:Distribution</code> here is narrower than in DCAT-2014 [[?VOCAB-DCAT-20140116]], where it also included APIs and feeds.