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...	...	@@ -98,9 +98,9 @@
98	98
99	99	For dataset versions that use male adult subjects, we can filter datasets using these 2 properties. The easiest way is to add a required filter to biological sex that is "EQUAL" to "male" and the age category "EQUAL" to "adult". By selecting the filter "EQUAL" instead of "CONTAINS", we ensure that only datasets with adult animals are found. If we want to be more general and include all subjects from the onset of sexual maturity, we can use "CONTAINS" instead as this will include subjects with the age category "prime adult", "young adult" and "late adult" as well.
100	100
101		-We are showing the unflattened query here. You can take advantage of the type filter (set it to subjects and subject groups), and flatten the query where possible. The query will look different; there will be multiple elements in the "path" for "biological sex" and for the "age category".
	101	+We have again taken advantage of the type filter (set it to subjects and subject groups), and we flattened the query where possible. This means that you now find multiple elements in the "path" for "biological sex" and for the "age category".
102	102
103		-Try it yourself and check out the differences between the results of the flattened and unflattened queries! Hint: look at the indexing of elements within your results!
	103	+Try it yourself and check out the differences between the results of the flattened and unflattened queries!
104	104
105	105	{{code language="json" layout="LINENUMBERS"}}
106	106	{
...	...	@@ -117,9 +117,8 @@
117	117	}
118	118	},
119	119	"meta": {
120		- "name": "FENS-example2",
121		- "responseVocab": "https://schema.hbp.eu/myQuery/",
122		- "type": "https://openminds.ebrains.eu/core/DatasetVersion"
	120	+ "type": "https://openminds.ebrains.eu/core/DatasetVersion",
	121	+ "responseVocab": "https://schema.hbp.eu/myQuery/"
123	123	},
124	124	"structure": [
125	125	{
...	...	@@ -155,35 +155,28 @@
155	155	},
156	156	{
157	157	"propertyName": "query:biologicalSex",
158		- "path": "https://openminds.ebrains.eu/vocab/biologicalSex",
159	159	"required": true,
160		- "structure": {
161		- "propertyName": "query:name",
162		- "path": "https://openminds.ebrains.eu/vocab/name",
163		- "filter": {
164		- "op": "EQUALS",
165		- "value": "male"
166		- }
167		- }
	158	+ "filter": {
	159	+ "op": "EQUALS",
	160	+ "value": "male"
	161	+ },
	162	+ "path": [
	163	+ "https://openminds.ebrains.eu/vocab/biologicalSex",
	164	+ "https://openminds.ebrains.eu/vocab/name"
	165	+ ]
168	168	},
169	169	{
170	170	"propertyName": "query:studiedState",
171		- "path": "https://openminds.ebrains.eu/vocab/studiedState",
172	172	"required": true,
173		- "structure": {
174		- "propertyName": "query:ageCategory",
175		- "path": "https://openminds.ebrains.eu/vocab/ageCategory",
176		- "required": true,
177		- "structure": {
178		- "propertyName": "query:name",
179		- "path": "https://openminds.ebrains.eu/vocab/name",
180		- "required": true,
181		- "filter": {
182		- "op": "EQUALS",
183		- "value": "adult"
184		- }
185		- }
186		- }
	170	+ "filter": {
	171	+ "op": "EQUALS",
	172	+ "value": "adult"
	173	+ },
	174	+ "path": [
	175	+ "https://openminds.ebrains.eu/vocab/studiedState",
	176	+ "https://openminds.ebrains.eu/vocab/ageCategory",
	177	+ "https://openminds.ebrains.eu/vocab/name"
	178	+ ]
187	187	}
188	188	]
189	189	}
...	...	@@ -264,7 +264,7 @@
264	264	}
265	265	{{/code}}
266	266
267		-**Query datasets based on content type**
	259	+ **Query datasets based on content type**
268	268
269	269	To find datasets using the content type structure, we can use a filter that "CONTAINS" a value (e.g. nifti). We are now not distinguishing between nifti 1 or nifti 2 files, but select all files that are classified as "nifti". If you want to narrow down the results further, you could replace "nifti" by "nifti.1" or "nifti.2".
270	270