Slight changes to services description

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

@@ -1,9 +1,34 @@
 <h3 class="manual-chapter-title">Introduction</h3>
+<h4>Introduction</h4>
 <p>
-  nopaque is a web-based digital working environment. It implements a
-  workflow based on the research process in the humanities and supports its
-  users in processing their data in order to subsequently apply digital
-  analysis methods to them. All processes are implemented in a specially
-  provided cloud environment with established open source software. This
-  always ensures that no personal data of the users is disclosed.
+  Nopaque is a web application that offers different services and tools to support 
+  researchers working with image and text-based data. These services are logically 
+  connected and build upon each other. They include:
 </p>
+ <ol style="list-style-type:disc; margin-left:2em; padding-bottom:0;">
+  <li><b>File setup</b>, which converts and merges different data  (e.g., books, letters) 
+  for further processing.</li>
+  <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</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 
+  computational linguistic data processing (tokenization, lemmatization, part-of-speech 
+  tagging and named-entity recognition.</li>
+  <li><b>Corpus analysis</b> makes use of CQP Query Language to search through text 
+  corpora with the aid of metadata and Natural Language Processing tags.</li>
+ </ol>
+
+Nopaque also features a <b>Social Area</b>, where researchers can create a personal profile, connect with other users and share corpora if desired.
+These services can be accessed from the sidebar in nopaque.
+All processes are implemented in a specially provided cloud environment with established open-source software. 
+This always ensures that no personal data of the users is disclosed.
+<p>
+*Note: the Transkribus HTR Pipeline is currently 
+deactivated; we are working on an alternative solution. You can try using Tesseract OCR, 
+though the results will likely be poor.
+</p>
+
+

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

@@ -0,0 +1,104 @@
+<h3 class="manual-chapter-title">Getting Started</h3>
+<h4>Getting Started</h4>
+<p>
+In this section, we will take you through all the steps you need to start analyzing your data with nopaque.
+</p>
+
+<div style="border: 1px solid; padding-left: 20px; margin-right: 400px; margin-bottom: 40px;">
+  <h5>Content</h5>
+  <ol style="list-style-type:disc">
+    <li><a href="#registration-and-login">Registration and login</a></li>
+    <li><a href="#preparing-files">Preparing files for analysis</a></li>
+    <li><a href="#converting-a-pdf-into-text">Converting a PDF into text data</a></li>
+    <li><a href="#extracting-linguistic-data">Extracting linguistic data from text</a></li>
+    <li><a href="#creating-a-corpus">Creating a corpus</a></li>
+    <li><a href="#analyzing-a-corpus">Analyzing a corpus</a></li>
+  </ol>
+</div>
+
+<p></p>
+
+<h5 id="registration-and-login">Registration and login</h5>
+<p>Before you can begin using nopaque, you will need to create a personal user account. 
+Open the menu (three dots) at the top right of the screen and choose “Register.” Enter 
+the required details listed on the registration page (username, password, email address). 
+After verifying your account via the link sent to your email, you can log in.</p>
+
+<h5 id="preparing-files">Preparing files for analysis</h5>
+<p>A few steps need to be taken before images, scans, or other text data are ready for 
+analysis in nopaque. The SpaCy NLP Pipeline service can only extract linguistic data 
+from texts in plain text (.txt) format. If your text is already in this format, you 
+can skip the next steps and go directly to <b>Extracting linguistic data from text</b>. 
+Otherwise, the next steps assume that you are starting off with image data.</p>
+<p>
+First, all data needs to be converted into PDF format. Using the <b>File Setup</b> service, 
+you can bundle images together – even of different formats – and convert them all into 
+one PDF file. Note that the File Setup service will sort the images based on their file 
+name in ascending order. It is thus recommended to name them accordingly, for example: 
+page-01.png, page-02.jpg, page-03.tiff.
+</p>
+<p>
+Add a title and description to your job and select the File Setup version* you want to use.
+After uploading the images and completing the File Setup job, the list of files added 
+can be seen under “Inputs.” Further below, under “Results,” you can find and download 
+the PDF output.</p>
+
+<h5 id="converting-a-pdf-into-text">Converting a PDF into text data</h5>
+<p>Select an image-to-text conversion tool depending on whether your PDF is primarily 
+composed of handwritten text or printed text. For printed text, select the <b>Tesseract OCR 
+Pipeline</b>. For handwritten text, select the <b>Transkribus HTR Pipeline</b>. Select the desired 
+language model or upload your own. Select the version* of Tesseract OCR you want to use 
+and click on submit to start the conversion. When the job is finished, various output 
+files can be seen and downloaded further below, under “Results.” You may want to review 
+the text output for errors and coherence. (Note: the Transkribus HTR Pipeline is currently 
+deactivated; we are working on an alternative solution. You can try using Tesseract OCR, 
+though the results will likely be poor.)
+</p>
+
+<h5 id="extracting-linguistic-data">Extracting linguistic data from text</h5>
+<p>The <b>SpaCy NLP Pipeline</b> service extracts linguistic information from plain text files 
+(in .txt format). 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>
+
+<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 <a href="{{ url_for('main.dashboard') }}">Dashboard</a> or 
+<a href="{{ url_for('services.corpus_analysis') }}">Corpus Analysis</a> sections under “My Corpora.” Click on “Create corpus” 
+and add a title and description for your corpus. After submitting, you will automatically 
+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 
+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 <b>Query Builder</b> (under <b>Concordance</b>) and the <b>Reader</b>. 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 <b>Concordance</b> 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>
+
+<br>
+<br>
+*For all services, it is recommended to use the latest version unless you need a model 
+only available in an earlier version or are looking to reproduce data that was originally generated 
+using an older version.

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

@@ -1,18 +0,0 @@
-<h3 class="manual-chapter-title">Registration and Log in</h3>
-<div class="row">
-  <div class="col s12 m4">
-    <img alt="Registration and Log in" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/registration-and-log-in.png') }}">
-  </div>
-  <div class="col s12 m8">
-    <p>
-      Before you can start using the web platform, you need to create a user
-      account. This requires only a few details: just a user name, an e-mail
-      address and a password are needed. In order to register yourself, fill out
-      the form on the <a href="{{ url_for('auth.register') }}">registration page</a>. After successful registration, the
-      created account must be verified. To do this, follow the instructions
-      given in the automatically sent e-mail. Afterwards, you can log in as
-      usual with your username/email address and password in the log-in form
-      located next to the registration button.
-    </p>
-  </div>
-</div>

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

@@ -1,15 +1,22 @@
 <h3 class="manual-chapter-title">Dashboard</h3>
+<h4>About the dashboard</h4>
+<br>
 <div class="row">
   <div class="col s12 m4">
     <img alt="Dashboard" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/dashboard.png') }}">
   </div>
   <div class="col s12 m8">
     <p>
-      The <a href="{{ url_for('main.dashboard') }}">dashboard</a> provides a central overview of all resources assigned to the
-      user. These are <a href="{{ url_for('main.dashboard', _anchor='corpora') }}">corpora</a> and created <a href="{{ url_for('main.dashboard', _anchor='jobs') }}">jobs</a>. Corpora are freely composable
-      annotated text collections and jobs are the initiated file processing
-      procedures. One can search for jobs as well as corpus listings using
-      the search field displayed above them.
+      The <a href="{{ url_for('main.dashboard') }}">dashboard</a> provides a central 
+      overview of all user-specific resources.
+      These are <a href="{{ url_for('main.dashboard', _anchor='corpora') }}">corpora</a>, 
+      created <a href="{{ url_for('main.dashboard', _anchor='jobs') }}">jobs</a>, and 
+      model <a href="{{ url_for('main.dashboard', _anchor='contributions') }}"">contributions</a>. 
+      A <b>corpus</b> is a freely composable annotated text collection. 
+      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. 
+      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

@@ -1,52 +1,107 @@
 <h3 class="manual-chapter-title">Services</h5>
+<h4>Services</h4>
+<p>
+In this section, we will describe the different services nopaque has to offer.
+</p>
+
 <div class="row">
   <div class="col s12 m4">
     <img alt="Services" class="materialboxed responsive-img" src="{{ url_for('static', filename='images/manual/services.png') }}">
   </div>
   <div class="col s12 m8">
     <p>
-      nopaque was designed from the ground up to be modular. This modularity
-      means that the offered workflow provides variable entry and exit points,
-      so that different starting points and goals can be flexibly addressed.
-      Each of these modules are implemented in a self-contained service, each of
-      which represents a step in the workflow. The services are coordinated in
-      such a way that they can be used consecutively. The order can either be
-      taken from the listing of the services in the left sidebar or from the
-      roadmap (accessible via the pink compass in the upper right corner). All
-      services are versioned, so the data generated with nopaque is always
+      Nopaque was designed to be modular. Its modules are implemented in 
+      self-contained <b>services</b>, each of which represents a step in the 
+      workflow. The typical workflow involves using services one after another, 
+      consecutively.
+      The typical workflow order can be taken from the listing of the 
+      services in the left sidebar or from the nopaque manual (accessible via the pink 
+      button in the upper right corner). 
+      The services can also be applied at different starting and ending points, 
+      which allows you to conduct your work flexibly.
+      All services are versioned, so the data generated with nopaque is always
       reproducible.
+      
+      <p>For all services, it is recommended to use the latest version (selected 
+      in the drop-down menu on the service page) unless you need a model 
+      only available in an earlier version or are looking to reproduce data that was originally generated 
+      using an older version.</p>
     </p>
   </div>
 </div>
 
-<h4 class="manual-chapter-title">File Setup</h4>
+
+
+<h4>File Setup</h4>
 <p>
   The <a href="{{ url_for('services.file_setup_pipeline') }}">File Setup Service</a> bundles image data, such as scans and photos,
   together in a handy PDF file. To use this service, use the job form to
   select the images to be bundled, choose the desired service version, and
-  specify a title and description. Please note that the service sorts the
-  images into the resulting PDF file based on the file names. So naming the
-  images correctly is of great importance. It has proven to be a good practice
-  to name the files according to the following scheme:
-  page-01.png, page-02.jpg, page-03.tiff, etc. In general, you can assume
+  specify a title and description.
+  Note that the File Setup service will sort the images based on their file name in 
+  ascending order. It is thus important and highly recommended to name 
+  them accordingly, for example: 
+  page-01.png, page-02.jpg, page-03.tiff. Generally, you can assume
   that the images will be sorted in the order in which the file explorer of
   your operating system lists them when you view the files in a folder
   sorted in ascending order by file name.
 </p>
 
 <h4>Optical Character Recognition (OCR)</h4>
-<p>Coming soon...</p>
+<p>
+  The <a href="{{ url_for('services.tesseract_ocr_pipeline') }}">Tesseract OCR Pipeline</a> 
+  converts image data - like photos and scans - into text data, making them machine-readable. 
+  This step enables you to proceed with the computational analysis of your documents. 
+  To use this service, use the job form to select the file you want to convert into text data. 
+  Then, choose the language model and service version you would like to use. Enter a title and description for your file and then 
+  submit your job. Once the job is finished, the results can be found and downloaded further below on the page, under 
+  the section labeled "Inputs."
+
+</p>
 
 <h4>Handwritten Text Recognition (HTR)</h4>
-<p>Coming soon...</p>
+<p>The Transkribus HTR Pipeline is currently 
+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 .txt file that you want to extract this information from. 
+Then select the language model and the version you want to use. Once the job is finished, you can find and download the files in 
+<b>.json</b> and <b>.vrt</b> format under the section labeled “Results.”</p>
 
 <h4>Corpus Analysis</h4>
 <p>
-  With the corpus analysis service, it is possible to create a text corpus
-  and then explore it in an analysis session. The analysis session is realized
+  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. 
+  </p>
+  <p>
+  With the <b>Reader</b>, 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 <b>Query Builder</b> section of the 
+  manual.
+  </p>
 </p>

app/templates/_base/_modals/_manual/08_cqp_query_language.html.j2

@@ -1,5 +1,22 @@
 <h3 class="manual-chapter-title">CQP Query Language</h3>
-<p>Within the Corpus Query Language, a distinction is made between two types of annotations: positional attributes and structural attributes. Positional attributes refer to a token, e.g. the word "book" is assigned the part-of-speech tag "NN", the lemma "book" and the simplified part-of-speech tag "NOUN" within the token structure. Structural attributes refer to text structure-giving elements such as sentence and entity markup. For example, the markup of a sentence is represented in the background as follows:</p>
+<h4 id="cqp-query-language">CQP Query Language</h4>
+<p>In this section, we will provide some functional explanations of the properties of the Corpus Query Language. This includes 
+the types of linguistic attributes one can work with and how to use them in your query.</p>
+
+<div style="border: 1px solid; padding-left: 20px; margin-right: 400px; margin-bottom: 40px;">
+  <h5>Content</h5>
+  <ol style="list-style-type:disc">
+    <li><a href="#overview-annotations">Overview of annotation types</a></li>
+    <li><a href="#positional-attributes">Positional attributes</a></li>
+    <li><a href="#searching-positional-attributes">How to search for positional attributes</a></li>
+    <li><a href="#structural-attributes">Structural attributes</a></li>
+    <li><a href="#searching-structural-attributes">How to search for structural attributes</a></li>
+
+  </ol>
+</div>
+
+<h4 id="overview-annotations">Overview of annotation types</h4>
+<p>Within the Corpus Query Language, a distinction is made between two types of annotations: <b>positional attributes</b> and <b>structural attributes</b>. Positional attributes refer to a token, e.g. the word "book" is assigned the part-of-speech tag "NN", the lemma "book" and the simplified part-of-speech tag "NOUN" within the token structure. Structural attributes refer to text structure-giving elements such as sentence and entity markup. For example, the markup of a sentence is represented in the background as follows:</p>
 <pre>
   <code>
     <span class="green-text">&lt;s&gt;                                     structural attribute</span>
@@ -13,7 +30,7 @@
   </code>
 </pre>
 
-<h4>Positional attributes</h4>
+<h4 id="positional-attributes">Positional attributes</h4>
 <p>Before you can start searching for positional attributes (also called tokens), it is necessary to know what properties they contain.</p>
 <ol>
   <li><span class="blue-text"><b>word</b></span>: The string as it is also found in the original text</li>
@@ -33,7 +50,7 @@
   </li>
 </ol>
 
-<h5>Searching for positional attributes</h5>
+<h5 id="searching-positional-attributes">How to search for positional attributes</h5>
 <div>
   <p>
     <b>Token with no condition on any property (also called <span class="blue-text">wildcard token</span>)</b><br>
@@ -118,7 +135,7 @@
   <pre style="margin-top: 0;"   ><code>         ^             ^ the braces indicate the start and end of an option group</code></pre>
 </div>
 
-<h4>Structural attributes</h4>
+<h4 id="structural-attributes">Structural attributes</h4>
 <p>nopaque provides several structural attributes for query. A distinction is made between attributes with and without value.</p>
 <ol>
   <li><span class="green-text"><b>s</b></span>: Annotates a sentence</li>
@@ -153,7 +170,7 @@
   </li>
 </ol>
 
-<h5>Searching for structural attributes</h5>
+<h5 id="searching-structural-attributes">How to search for structural attributes</h5>
 <pre><code>&lt;ent&gt; [] &lt;/ent&gt;;                       A one token long entity of any type</code></pre>
 <pre><code>&lt;ent_type="PERSON"&gt; [] &lt;/ent_type&gt;;     A one token long entity of type PERSON</code></pre>
 <pre><code>&lt;ent_type="PERSON"&gt; []* &lt;/ent_type&gt;;    Entity of any length of type PERSON</code></pre>

app/templates/_base/_modals/_manual/09_query_builder.html.j2

@@ -1,27 +1,12 @@
 <h3 class="manual-chapter-title">Query Builder Tutorial</h3>
-<h4>Overview</h4>
-<p>The query builder can be accessed via "My Corpora" or "Corpus Analysis" in the sidebar options. 
-Select the desired corpus and click on the "Analyze" and then "Concordance"
-buttons to open the query builder.</p>
-<p>The query builder uses the Corpus Query Language (CQL) to help you make a query for analyzing your texts. 
-In this way, it is possible to filter out various types of text parameters, for 
-example, a specific word, a lemma, or you can set part-of-speech 
-tags (pos) that indicate the type of word you are looking for (a noun, an 
-adjective, etc.). In addition, you can also search for structural attributes, 
-or specify your query for a token (word, lemma, pos) via entity typing. And of 
-course, the different text parameters can be combined.</p>
-<p>Tokens and structural attributes can be added by clicking on the "+" button
-(the "input marker") in the input field or the labeled buttons below it. Elements 
-added are shown as chips. These can be reorganized using drag and drop. The input 
-marker can also be moved in this way. Its position shows where new elements will be added. <br>
-A "translation" of your query into Corpus Query Language (CQL) is shown below.</p>
-<p>Advanced users can make direct use of the Corpus Query Language (CQL) by switching to "expert mode" via the toggle button.</p>
-<p>The entire input field can be cleared using the red trash icon on the right.</p>
-<br>
+<h4>Query Builder</h4>
+<p>In this section, we will provide you with more detailed instructions on how to use the Query Builder - 
+nopaque's main user-friendly tool for finding and analyzing different linguistic elements of your texts.</p>
 
 <div style="border: 1px solid; padding-left: 20px; margin-right: 400px; margin-bottom: 40px;">
   <h5>Content</h5>
   <ol style="list-style-type:disc">
+    <li><a href="#general-overview">General Overview</a></li>
     <li><a href="#add-new-token-tutorial">Add a new token to your query</a></li>
     <li><a href="#edit-options-tutorial">Options for editing your query</a></li>
     <li><a href="#add-structural-attribute-tutorial">Add structural attributes to your query</a></li>
@@ -29,6 +14,33 @@ A "translation" of your query into Corpus Query Language (CQL) is shown below.</
   </ol>
 </div>
 
+<h4 id="general-overview">General Overview</h4>
+<p>The Query Builder can be accessed via <a href=" {{ url_for('main.dashboard') }}">My Corpora</a> or <a href=" {{ url_for('services.corpus_analysis') }}">Corpus Analysis</a> in the sidebar options. 
+Click on the corpus you wish to analyze. You will be sent to its corpus overview page.
+Here, click on <b>Analyze</b> to reach the analysis page.
+The analysis page features different options for analyzing your corpus, including 
+visualizations and a <b>Reader</b> module. In this case, we want to open the query builder. 
+To do so, click on the <b>Concordance</b> button on the top of the page.</p>
+<p>The query builder uses the <b>Corpus Query Language (CQL)</b> to help you make a query for analyzing your texts.
+In this way, it is possible to filter out various types of text parameters, for 
+example, a specific word, a lemma, or you can set part-of-speech 
+tags (pos) that indicate the type of word you are looking for (a noun, an 
+adjective, etc.). In addition, you can also search for structural attributes, 
+or specify your query for a token (word, lemma, pos) via entity typing. And of 
+course, the different text parameters can be combined.</p>
+<p>Tokens and structural attributes can be added by clicking on the <b>"+"</b> button
+(what we call the "input marker") in the input field or the labeled buttons below it. Elements 
+added are shown as chips. These can be reorganized using drag and drop. The input 
+marker can also be moved in this way. Its position shows where new elements will be added. <br>
+A "translation" of your query into Corpus Query Language (CQL) will be displayed underneath the query field.</p>
+<p>For more information, see our <b>manual section for the Corpus Query Language.</b>
+<br>
+Advanced users can make direct use of CQL by switching to <b>expert mode</b> via the toggle button.
+</p>
+<p>The entire input field can be cleared using the red trash icon on the right.</p>
+<br>
+
+
 {# Add Token Tutorial #}
 <div>
   <hr>
@@ -37,8 +49,8 @@ A "translation" of your query into Corpus Query Language (CQL) is shown below.</
   <h4 id="add-new-token-tutorial">Add new token to your Query</h4>
   <p>If you are only looking for a specific token, you can click on the left 
   button and select the type of token you are looking for from the drop-down menu. 
-  By default "Word" is selected. </p>
-  <br>
+  "Word" is selected by default. </p>
+  
   <h5>Word and Lemma</h5>
   <p>If you want to search for a specific word or lemma and the respective 
   category is selected in the drop-down menu, you can type in the word or lemma 

app/templates/_base/_modals/manual.html.j2

@@ -3,21 +3,22 @@
     <h2>Manual</h2>
     <ul class="tabs" id="manual-modal-toc">
       <li class="tab"><a href="#manual-modal-introduction">Introduction</a></li>
-      <li class="tab"><a href="#manual-modal-registration-and-log-in">Registration and Log in</a></li>
+      <li class="tab"><a href="#manual-modal-getting-started">Getting Started</a></li>
       <li class="tab"><a href="#manual-modal-dashboard">Dashboard</a></li>
       <li class="tab"><a href="#manual-modal-services">Services</a></li>
-      <li class="tab"><a href="#manual-modal-a-closer-look-at-the-corpus-analysis">A closer look at the Corpus Analysis</a></li>
-      <li class="tab"><a href="#manual-modal-cqp-query-language">CQP Query Language</a></li>
+      <!-- <li class="tab"><a href="#manual-modal-a-closer-look-at-the-corpus-analysis">A closer look at the Corpus Analysis</a></li> -->
       <li class="tab"><a href="#manual-modal-query-builder">Query Builder</a></li>
+      <li class="tab"><a href="#manual-modal-cqp-query-language">CQP Query Language</a></li>
+
       <li class="tab"><a href="#manual-modal-tagsets">Tagsets</a></li>
     </ul>
     <div id="manual-modal-introduction">
       <br>
       {% include "_base/_modals/_manual/01_introduction.html.j2" %}
     </div>
-    <div id="manual-modal-registration-and-log-in">
+    <div id="manual-modal-getting-started">
       <br>
-      {% include "_base/_modals/_manual/02_registration_and_log_in.html.j2" %}
+      {% include "_base/_modals/_manual/02_getting_started.html.j2" %}
     </div>
     <div id="manual-modal-dashboard">
       <br>
@@ -27,10 +28,10 @@
       <br>
       {% include "_base/_modals/_manual/06_services.html.j2" %}
     </div>
-    <div id="manual-modal-a-closer-look-at-the-corpus-analysis">
+    <!-- <div id="manual-modal-a-closer-look-at-the-corpus-analysis">
       <br>
       {% include "_base/_modals/_manual/07_a_closer_look_at_the_corpus_analysis.html.j2" %}
-    </div>
+    </div> -->
     <div id="manual-modal-cqp-query-language">
       <br>
       {% include "_base/_modals/_manual/08_cqp_query_language.html.j2" %}