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 (intro_analysis_workflows)=
 # Introduction
 
-In this section, we provide guidelines on documenting and sharing image analysis workflows to ensure they are reproducible as well as reusable.  
-<!--Notes which will not be shown on the actual page-->
+The primary aim of ensuring the reproducibility of bioimage analysis workflow used in life science publications is to allow peers to examine the exact details of the image processing and analysis used in the publication. Computer code,  e.g. ImageJ macro, Python script, Pipeline in CellProfiler, Workflow in KNIME, Rscript, is the perfect form of this description. “**Minimal**” is a set of essential elements for running this code, the “**Recommended**” requirements are a more optimized set of elements for the examination of the workflow, and “**Ideal**” is a highly appreciated set of elements to minimize the stress of examining the workflow. 
+
+Note that the reusability of workflow, such as how well the workflow is implemented for the  “user experience”, is a different aspect we have not included in determining this checklist. The checklist focuses on how well the image analysis methods are reported for the examination of its scientific adequacy.
+
+Here, “ workflow” is defined as described in [1]:
+
+> To scientifically analyze and address an underlying biological problem, one needs to hand-pick some algorithms from these collections, carefully adjust their functional parameters to the problem and assemble them in a meaningful order. Such a sequence of image processing algorithms with a specified parameter set is what we call a **“workflow”**.
+
+The following three categories of workflows are used in the checklist as requirements are different:
+
+1. **Established workflows or workflow templates**: workflows available in the scientific literature or well-established in the respective fields.
+2. **Novel workflows**: established or new image analysis components (available in software platforms or libraries) are assembled by researchers into a novel workflow.
+3. **Machine learning (ML) workflows**: ML uses an extended technical terminology and ML workflows that utilize deep neural networks (‘deep learning’) face unique challenges with respect to reproducibility. Given the rapid advancements in this field, we created a separate ML checklist. 
+
+### References
+
+[1] Miura, K., P. Paul-Gilloteaux, S. Tosi, and J. Colombelli. 2020. Workflows and Components of Bioimage Analysis. In Bioimage Data Analysis Workflows. Springer International Publishing, https://doi.org/10.1007/978-3-030-22386-1_1
+
+
+
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@@ -398,6 +400,14 @@ <h1>Introduction</h1>
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+            <div>
+                <h2> Contents </h2>
+            </div>
+            <nav aria-label="Page">
+                <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#references">References</a></li>
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@@ -409,8 +419,23 @@ <h1>Introduction</h1>
 
   <section class="tex2jax_ignore mathjax_ignore" id="introduction">
 <span id="intro-analysis-workflows"></span><h1>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">#</a></h1>
-<p>In this section, we provide guidelines on documenting and sharing image analysis workflows to ensure they are reproducible as well as reusable.</p>
-<!--Notes which will not be shown on the actual page--></section>
+<p>The primary aim of ensuring the reproducibility of bioimage analysis workflow used in life science publications is to allow peers to examine the exact details of the image processing and analysis used in the publication. Computer code,  e.g. ImageJ macro, Python script, Pipeline in CellProfiler, Workflow in KNIME, Rscript, is the perfect form of this description. “<strong>Minimal</strong>” is a set of essential elements for running this code, the “<strong>Recommended</strong>” requirements are a more optimized set of elements for the examination of the workflow, and “<strong>Ideal</strong>” is a highly appreciated set of elements to minimize the stress of examining the workflow.</p>
+<p>Note that the reusability of workflow, such as how well the workflow is implemented for the  “user experience”, is a different aspect we have not included in determining this checklist. The checklist focuses on how well the image analysis methods are reported for the examination of its scientific adequacy.</p>
+<p>Here, “ workflow” is defined as described in [1]:</p>
+<blockquote>
+<div><p>To scientifically analyze and address an underlying biological problem, one needs to hand-pick some algorithms from these collections, carefully adjust their functional parameters to the problem and assemble them in a meaningful order. Such a sequence of image processing algorithms with a specified parameter set is what we call a <strong>“workflow”</strong>.</p>
+</div></blockquote>
+<p>The following three categories of workflows are used in the checklist as requirements are different:</p>
+<ol class="arabic simple">
+<li><p><strong>Established workflows or workflow templates</strong>: workflows available in the scientific literature or well-established in the respective fields.</p></li>
+<li><p><strong>Novel workflows</strong>: established or new image analysis components (available in software platforms or libraries) are assembled by researchers into a novel workflow.</p></li>
+<li><p><strong>Machine learning (ML) workflows</strong>: ML uses an extended technical terminology and ML workflows that utilize deep neural networks (‘deep learning’) face unique challenges with respect to reproducibility. Given the rapid advancements in this field, we created a separate ML checklist.</p></li>
+</ol>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
+<p>[1] Miura, K., P. Paul-Gilloteaux, S. Tosi, and J. Colombelli. 2020. Workflows and Components of Bioimage Analysis. In Bioimage Data Analysis Workflows. Springer International Publishing, <a class="reference external" href="https://doi.org/10.1007/978-3-030-22386-1_1">https://doi.org/10.1007/978-3-030-22386-1_1</a></p>
+   <!--Notes which will not be shown on the actual page--></section>
+</section>
 
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