diff --git a/Python_html/21_Transforms_and_Resampling.html b/Python_html/21_Transforms_and_Resampling.html
index 471bcc67..550cd355 100644
--- a/Python_html/21_Transforms_and_Resampling.html
+++ b/Python_html/21_Transforms_and_Resampling.html
@@ -8954,7 +8954,7 @@ <h2 id="Homework:-creating-a-color-mesh"><font color="red">Homework:</font> crea
 <ol>
 <li>Implement the <a href="https://en.wikipedia.org/wiki/Marching_cubes">marching cubes algorithm</a> to obtain the set of triangles corresponding to the iso-surface of structures of interest (skin, white matter,...).</li>
 <li>Find the color associated with each of the triangle vertices using the code above.</li>
-<li>Save the data using the ASCII version of the <a href="https://en.wikipedia.org/wiki/PLY_(file_format">PLY</a>), Polygon File Format (a.k.a. Stanford Triangle Format).</li>
+<li>Save the data using the ASCII version of the (<a href="https://en.wikipedia.org/wiki/PLY_(file_format)">PLY</a>), Polygon File Format (a.k.a. Stanford Triangle Format).</li>
 <li>Use <a href="http://www.meshlab.net/">meshlab</a> to view your creation.</li>
 </ol>
 </div>
diff --git a/Python_html/71_Trust_But_Verify.html b/Python_html/71_Trust_But_Verify.html
index f6768a76..093fd1fa 100644
--- a/Python_html/71_Trust_But_Verify.html
+++ b/Python_html/71_Trust_But_Verify.html
@@ -7671,7 +7671,7 @@ <h2 id="Data">Data<a class="anchor-link" href="#Data">¶</a></h2><p>For convenie
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-<h2 id="Characterizing--image-set">Characterizing  image set<a class="anchor-link" href="#Characterizing--image-set">¶</a></h2><p>To characterize the image set we have written a <a href="scripts/characterize_data.py">Python script</a> that you should run from the command line. This script is very flexible and allows you to robustly characterize your image set. Try the various options and learn more about your data. You'd be surprised how many times the data isn't what you thought it is when only relying on visual inspection. The script allows you to inspect your data both on a file by file basis and as DICOM series where an image (volume) is stored in multiple files.</p>
+<h2 id="Characterizing--image-set">Characterizing  image set<a class="anchor-link" href="#Characterizing--image-set">¶</a></h2><p>To characterize the image set we have written a <a href="https://github.com/InsightSoftwareConsortium/SimpleITK-Notebooks/blob/master/Python/scripts/characterize_data.py">Python script</a> that you should run from the command line. This script is very flexible and allows you to robustly characterize your image set. Try the various options and learn more about your data. You'd be surprised how many times the data isn't what you thought it is when only relying on visual inspection. The script allows you to inspect your data both on a file by file basis and as DICOM series where an image (volume) is stored in multiple files.</p>
 <p>File by file:</p>
 <pre><code>python characterize_data.py data output/generic_image_data_report.csv per_file \
 --imageIO "" --external_applications ./dciodvfy --external_applications_headings "DICOM Compliant" \