more manual updates

app/templates/_base/_modals/_manual/01_introduction.html.j2

@@ -11,7 +11,7 @@
   <li><b>Image-to-text conversion tools:</b></li>
     <ol style="list-style-type:circle; margin-left:1em; padding-bottom:0;"><li><b>Optical Character Recognition</b> converts photos and 
     scans into text data, making them machine-readable.</li>
-    <li><b>Transkribus HTR (Handwritten Text Recognition) Pipeline (currently deactivated)* </b> 
+    <li><b>Transkribus HTR (Handwritten Text Recognition) Pipeline</b> (currently deactivated)* 
     also converts images into text data, making them machine-readable.</li>
     </ol>
   <li><b>Natural Language Processing</b> extracts information from your text via 

app/templates/_base/_modals/_manual/02_getting_started.html.j2

@@ -57,10 +57,21 @@ version* you want to use. When the job is finished, find and download the files
 <b>.json</b> and <b>.vrt</b> format under “Results.”</p>
 <h5 id="creating-a-corpus">Creating a corpus</h5>
 <p>Now, using the files in .vrt format, you can create a corpus. This can be done 
-in the Dashboard or Corpus Analysis under “My Corpora.” Click on “Create corpus” 
+in the <a href="{{ url_for('main.dashboard') }}">Dashboard</a> or 
-and add a title and description for your corpus. After submitting, navigate down to 
+<a href="{{ url_for('services.corpus_analysis') }}">Corpus Analysis</a> sections under “My Corpora.” Click on “Create corpus” 
-the “Corpus files” section. Once you have added the desired .vrt files, select “Build” 
+and add a title and description for your corpus. After submitting, you will automatically 
-on the corpus page under “Actions.” Now, your corpus is ready for analysis.</p>
+be taken to the corpus overview page (which can be called up again via the corpus lists) 
+of your new, still empty corpus. </p>
+<p>
+Further down in the “Corpus files” section, you can add texts in .vrt format 
+(results of the NLP service) to your new corpus. To do this, use the "Add Corpus File" 
+button and fill in the form that appears. Here, you can add 
+metadata to each text. After adding all texts to the corpus, it must 
+be prepared for analysis. This process can be initiated by clicking on the 
+"Build" button under "Actions". 
+On the corpus overview page, you can see information about the current status of 
+the corpus in the upper right corner. After the build process, the status "built" should be shown here.
+Now, your corpus is ready for analysis.</p>
 <h5 id="analyzing-a-corpus">Analyzing a corpus</h5>
 <p>Navigate to the corpus you would like to analyze and click on the Analyze button. 
 This will take you to an analysis overview page for your corpus. Here, you can find a 

app/templates/_base/_modals/_manual/03_dashboard.html.j2

@@ -16,7 +16,7 @@
       A <b>job</b> is an initiated file processing procedure. 
       A <b>model</b> is a mathematical system for pattern recognition based on data examples that have been processed by AI. One can search for jobs as 
       well as corpus listings using the search field displayed above them on the dashboard. 
-      Models can be found and edited by clicking on the corresponding service under <b>My Contributions</b>.      
+      Uploaded models can be found and edited by clicking on the corresponding service under <b>My Contributions</b>.      
     </p>
   </div>
   <div class="col s12">&nbsp;</div>

app/templates/_base/_modals/_manual/06_services.html.j2

@@ -61,12 +61,41 @@ deactivated. We are working on an alternative solution. In the meantime, you can
 try using Tesseract OCR, though the results will likely be poor.</p>
 
 <h4>Natural Language Processing (NLP)</h4>
-<p>Coming soon...</p>
+<p>The <a href="{{ url_for('services.spacy_nlp_pipeline') }}">SpaCy NLP Pipeline</a> extracts 
+information from plain text files (.txt format) via computational linguistic data processing 
+(tokenization, lemmatization, part-of-speech tagging and named-entity recognition). 
+To use this service, select the corresponding .txt file, the language model, and the 
+version you want to use. When the job is finished, find and download the files in 
+<b>.json</b> and <b>.vrt</b> format under “Results.”</p>
 
 <h4>Corpus Analysis</h4>
 <p>
-  With the corpus analysis service, it is possible to create a text corpus
+  With the <a href="{{ url_for('services.corpus_analysis') }}">Corpus Analysis</a> 
+  service, it is possible to create a text corpus
   and then explore through it with analytical tools. The analysis session is realized
   on the server side by the Open Corpus Workbench software, which enables
-  efficient and complex searches with the help of the CQP Query Language.
+  efficient and complex searches with the help of the CQP Query Language.</p>
+  <p>
+  To use this service, navigate to the corpus you would like to analyze and click on the Analyze button. 
+  This will take you to an analysis overview page for your corpus. Here, you can find 
+  a visualization of general linguistic information of your corpus, including tokens, 
+  sentences, unique words, unique lemmas, unique parts of speech and unique simple 
+  parts of speech. You will also find a pie chart of the proportional textual makeup 
+  of your corpus and can view the linguistic information for each individual text file. 
+  A more detailed visualization of token frequencies with a search option is also on 
+  this page.
+  </p>
+  <p>
+  From the corpus analysis overview page, you can navigate to other analysis modules: 
+  the Query Builder (under Concordance) and the Reader. With the Reader, you can read 
+  your corpus texts tokenized with the associated linguistic information. The tokens 
+  can be shown as lemmas, parts of speech, words, and can be displayed in different 
+  ways: visually as plain text with the option of highlighted entities or as chips.
+  </p>
+  <p>
+  The Concordance module allows for more specific, query-oriented text analyses. 
+  Here, you can filter out text parameters and structural attributes in different 
+  combinations. This is explained in more detail in the Query Builder section of the 
+  manual.
+  </p>
 </p>