diff --git a/.nojekyll b/.nojekyll
index f45c2e4..330da08 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-844b6b6e
\ No newline at end of file
+a265b18f
\ No newline at end of file
diff --git a/about/about.html b/about/about.html
index 7fc8a66..5957be8 100644
--- a/about/about.html
+++ b/about/about.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/access/Computerome.html b/access/Computerome.html
index a39f508..457bae5 100644
--- a/access/Computerome.html
+++ b/access/Computerome.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/access/UCloud.html b/access/UCloud.html
index 7a2cf09..dee5206 100644
--- a/access/UCloud.html
+++ b/access/UCloud.html
@@ -110,6 +110,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
@@ -954,7 +958,7 @@ <h3 class="anchored" data-anchor-id="step-6"><strong>Step 6</strong></h3>
 });
 </script>
 </div> <!-- /content -->
-<script>var lightboxQuarto = GLightbox({"openEffect":"zoom","closeEffect":"zoom","selector":".lightbox","loop":false,"descPosition":"bottom"});
+<script>var lightboxQuarto = GLightbox({"loop":false,"closeEffect":"zoom","selector":".lightbox","descPosition":"bottom","openEffect":"zoom"});
 (function() {
   let previousOnload = window.onload;
   window.onload = () => {
diff --git a/access/genomedk.html b/access/genomedk.html
index 6a44af7..e50edb8 100644
--- a/access/genomedk.html
+++ b/access/genomedk.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/access/index.html b/access/index.html
index 629dab9..43b2e55 100644
--- a/access/index.html
+++ b/access/index.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/access/other.html b/access/other.html
index 9672d43..9858d98 100644
--- a/access/other.html
+++ b/access/other.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/cards/AlbaMartinez.html b/cards/AlbaMartinez.html
index ee00d3f..b6e3327 100644
--- a/cards/AlbaMartinez.html
+++ b/cards/AlbaMartinez.html
@@ -108,6 +108,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/cards/JacobHansen.html b/cards/JacobHansen.html
index c3f7e22..a0b4fd2 100644
--- a/cards/JacobHansen.html
+++ b/cards/JacobHansen.html
@@ -108,6 +108,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/cards/JakobSkelmose.html b/cards/JakobSkelmose.html
index 33ca665..acee4b3 100644
--- a/cards/JakobSkelmose.html
+++ b/cards/JakobSkelmose.html
@@ -108,6 +108,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/cards/JenniferBartell.html b/cards/JenniferBartell.html
index 6583825..9d7605a 100644
--- a/cards/JenniferBartell.html
+++ b/cards/JenniferBartell.html
@@ -108,6 +108,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/cards/SamueleSoraggi.html b/cards/SamueleSoraggi.html
index c9eb136..581e2f3 100644
--- a/cards/SamueleSoraggi.html
+++ b/cards/SamueleSoraggi.html
@@ -108,6 +108,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/contact/contact.html b/contact/contact.html
index f3bc0f7..a9bbd35 100644
--- a/contact/contact.html
+++ b/contact/contact.html
@@ -167,6 +167,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
@@ -319,7 +323,7 @@ <h1 class="title">Contact the Sandbox</h1>
 
 <div class="quarto-listing quarto-listing-container-grid" id="listing-listing">
 <div class="list grid quarto-listing-cols-5">
-<div class="g-col-1" data-index="0" data-listing-file-modified-sort="1723542842593" data-listing-reading-time-sort="1" data-listing-word-count-sort="47">
+<div class="g-col-1" data-index="0" data-listing-file-modified-sort="1724236202083" data-listing-reading-time-sort="1" data-listing-word-count-sort="47">
 <a href="../cards/AlbaMartinez.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="../cards/alba.jpg" style="height: 300px;"  class="thumbnail-image card-img"/></p>
@@ -334,7 +338,7 @@ <h5 class="no-anchor card-title listing-title">
 </div>
 </a>
 </div>
-<div class="g-col-1" data-index="1" data-listing-file-modified-sort="1723542842593" data-listing-reading-time-sort="1" data-listing-word-count-sort="93">
+<div class="g-col-1" data-index="1" data-listing-file-modified-sort="1724236202083" data-listing-reading-time-sort="1" data-listing-word-count-sort="93">
 <a href="../cards/JacobHansen.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="../cards/jaCob.jpg" style="height: 300px;"  class="thumbnail-image card-img"/></p>
@@ -349,7 +353,7 @@ <h5 class="no-anchor card-title listing-title">
 </div>
 </a>
 </div>
-<div class="g-col-1" data-index="2" data-listing-file-modified-sort="1723542842593" data-listing-reading-time-sort="1" data-listing-word-count-sort="41">
+<div class="g-col-1" data-index="2" data-listing-file-modified-sort="1724236202083" data-listing-reading-time-sort="1" data-listing-word-count-sort="41">
 <a href="../cards/JakobSkelmose.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="../cards/jaKob.jpg" style="height: 300px;"  class="thumbnail-image card-img"/></p>
@@ -364,7 +368,7 @@ <h5 class="no-anchor card-title listing-title">
 </div>
 </a>
 </div>
-<div class="g-col-1" data-index="3" data-listing-file-modified-sort="1723542842593" data-listing-reading-time-sort="1" data-listing-word-count-sort="133">
+<div class="g-col-1" data-index="3" data-listing-file-modified-sort="1724236202083" data-listing-reading-time-sort="1" data-listing-word-count-sort="133">
 <a href="../cards/JenniferBartell.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="../cards/jennie.jpg" style="height: 300px;"  class="thumbnail-image card-img"/></p>
@@ -379,7 +383,7 @@ <h5 class="no-anchor card-title listing-title">
 </div>
 </a>
 </div>
-<div class="g-col-1" data-index="4" data-listing-file-modified-sort="1723542842593" data-listing-reading-time-sort="1" data-listing-word-count-sort="53">
+<div class="g-col-1" data-index="4" data-listing-file-modified-sort="1724236202083" data-listing-reading-time-sort="1" data-listing-word-count-sort="53">
 <a href="../cards/SamueleSoraggi.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="../cards/samuele.jpg" style="height: 300px;"  class="thumbnail-image card-img"/></p>
diff --git a/contributors.html b/contributors.html
index 7467baf..b9cba61 100644
--- a/contributors.html
+++ b/contributors.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="./modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="./workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/css/styles.css b/css/styles.css
index fe39df7..5cbabda 100644
--- a/css/styles.css
+++ b/css/styles.css
@@ -3,3 +3,8 @@
     max-width: 50%; /* Make the image responsive */
     height: 50%; /* Maintain aspect ratio */
   }
+
+  .layout-border {
+    background-color:  #f0f0f0;
+    padding: 20px;
+  }
\ No newline at end of file
diff --git a/datasets/datapolicy.html b/datasets/datapolicy.html
index 9e11750..adc7c33 100644
--- a/datasets/datapolicy.html
+++ b/datasets/datapolicy.html
@@ -108,6 +108,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
@@ -195,7 +199,7 @@ <h1 class="title">Data policy</h1>
     <div>
     <div class="quarto-title-meta-heading">Modified</div>
     <div class="quarto-title-meta-contents">
-      <p class="date-modified">August 13, 2024</p>
+      <p class="date-modified">August 21, 2024</p>
     </div>
   </div>
     
diff --git a/datasets/datasets.html b/datasets/datasets.html
index a40fd91..c617ca3 100644
--- a/datasets/datasets.html
+++ b/datasets/datasets.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/datasets/index.html b/datasets/index.html
index 9ed2fdf..16d2100 100644
--- a/datasets/index.html
+++ b/datasets/index.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/datasets/synthdata.html b/datasets/synthdata.html
index 0b0277a..20deb5f 100644
--- a/datasets/synthdata.html
+++ b/datasets/synthdata.html
@@ -108,6 +108,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
@@ -199,7 +203,7 @@ <h1 class="title">Synthetic data</h1>
     <div>
     <div class="quarto-title-meta-heading">Modified</div>
     <div class="quarto-title-meta-contents">
-      <p class="date-modified">August 13, 2024</p>
+      <p class="date-modified">August 21, 2024</p>
     </div>
   </div>
     
diff --git a/develop/news/news.html b/develop/news/news.html
index 5a5d756..284d402 100644
--- a/develop/news/news.html
+++ b/develop/news/news.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/index.html b/index.html
index 3042296..18cfb3b 100644
--- a/index.html
+++ b/index.html
@@ -108,6 +108,10 @@
   <li class="nav-item">
     <a class="nav-link" href="./modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="./workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
@@ -176,7 +180,7 @@ <h3 class="anchored" data-anchor-id="access-our-training-modules">Access our tra
 <div>
 
 </div>
-<div class="layout-border quarto-layout-panel" data-layout-ncol="5" data-layout-nrow="1">
+<div class="layout-border quarto-layout-panel" data-layout-nrow="1" data-layout-ncol="5">
 <div class="quarto-layout-row">
 <div class="column quarto-layout-cell" style="flex-basis: 20.0%;justify-content: flex-start;">
 <p><a href="https://hds-sandbox.github.io/modules/#sandbox-hpc"><img src="../images/HPC.png" class="hover-target img-fluid" style="width:100.0%" alt="HPC Lab"></a> <strong><span style="display: block; margin-top:15px; text-align: center; font-size: 1.2em;">HPC Lab</span></strong></p>
diff --git a/listings.json b/listings.json
index bbd0983..c6aa069 100644
--- a/listings.json
+++ b/listings.json
@@ -1,4 +1,14 @@
 [
+  {
+    "listing": "/contact/contact.html",
+    "items": [
+      "/cards/AlbaMartinez.html",
+      "/cards/JacobHansen.html",
+      "/cards/JakobSkelmose.html",
+      "/cards/JenniferBartell.html",
+      "/cards/SamueleSoraggi.html"
+    ]
+  },
   {
     "listing": "/news.html",
     "items": [
@@ -30,15 +40,5 @@
       "/news/past/2022-04-22-basicpm-wrapup.html",
       "/news/past/2022-01-04-basicpm.html"
     ]
-  },
-  {
-    "listing": "/contact/contact.html",
-    "items": [
-      "/cards/AlbaMartinez.html",
-      "/cards/JacobHansen.html",
-      "/cards/JakobSkelmose.html",
-      "/cards/JenniferBartell.html",
-      "/cards/SamueleSoraggi.html"
-    ]
   }
 ]
\ No newline at end of file
diff --git a/modules/AlphaFold_0122.html b/modules/AlphaFold_0122.html
index bfb7f64..f93fb3b 100644
--- a/modules/AlphaFold_0122.html
+++ b/modules/AlphaFold_0122.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/modules/EHRs.html b/modules/EHRs.html
index 44ba36d..54ad8b2 100644
--- a/modules/EHRs.html
+++ b/modules/EHRs.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/modules/bulk_rnaseq.html b/modules/bulk_rnaseq.html
index 03725be..1723492 100644
--- a/modules/bulk_rnaseq.html
+++ b/modules/bulk_rnaseq.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
diff --git a/modules/clinProteomics_0122.html b/modules/clinProteomics_0122.html
index 66412ab..4707c0b 100644
--- a/modules/clinProteomics_0122.html
+++ b/modules/clinProteomics_0122.html
@@ -107,6 +107,10 @@
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     "text": "Discussion and feedback\nWe hope you enjoyed the live demo. If you have broader questions, suggestions, or concerns, now is the time to raise them! If you are totally toast for the day, remember that you can check out longer versions of our tutorials as well as other topics and tools in each of the Sandbox modules or join us for a multi-day in person course.\nAs data scientists, we also would be really happy for some quantifiable info and feedback - we want to build things that the Danish health data science community is excited to use. Please answer these 5 questions for us before you head out for the day (link activated on day of the workshop).\n\nNice meeting you and we hope to see you again!"
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-    "text": "The Sandbox concept\nThe Health Data Science Sandbox aims to be a training resource for bioinformaticians, data scientists, and those generally curious about how to investigate large biomedical datasets. We are an active and developing project seeking interested users (both trainees and educators). All of our open-source materials are available on our Github page and much more information is available on the rest of the website you are currently visiting! We work with both UCloud and Computerome (major Danish academic supercomputers) - see our HPC Access page for more info on each set up."
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-    "text": "Access Sandbox resources\nWe currently provide training materials and resources as topical apps on UCloud, the supercomputer located at the University of Southern Denmark. To use these resources, you’ll need the following:\n\nLog onto UCloud at the address http://cloud.sdu.dk using your university credentials.\nthe ability to navigate in linux / RStudio / Jupyter. You don’t need to be an expert, but it is beyond our ambitions (and course material) to teach you how to code and how to run analyses simultaneously. We recommend a basic R or Python course before diving in.\n\nNote:\n\nTo use Sandbox materials outside of the workshop, you can request a project by clicking on apply for resources in your uCloud dashboard.\nIf you are a BSc or MSc student, you need a supervisor to apply on your behalf, or you can try to apply yourself mentioning the supervisor approval in the application.\nRemember, however, that you have 1000Kr of computing credit, and around 50GB of free storage to work on uCLoud."
+    "text": "Access Sandbox resources\nOur first choice is to provide all the training materials, tutorials, and tools as interactive apps on UCloud, the supercomputer located at the University of Southern Denmark. Anyone using these resources needs the following:\n\na Danish university ID so you can sign on to UCloud via WAYF1.\n\n \n\n for UCloud Access click here \n\n \n\nbasic ability to navigate in Linux/RStudio/Jupyter. You don’t need to be an expert, but it is beyond our ambitions (and course material) to teach you how to code from zero and how to run analyses simultaneously. We recommend a basic R or Python course before diving in.\nFor workshop participants: Use our invite link to the correct UCloud workspace that will be shared on the day of the workshop. This way, we can provide you with compute resources for the active sessions of the workshop2 Click the link below after your first uCloud access and accept the invite that shows.\n\n \n\n Invite link to uCloud workspace \n\n   \n\n\n\n\n\n\nNote\n\n\n\nOur apps can run on other clusters, simply by pulling a so-called docker container. You only need to have either docker or singularity installed on the cluster. GenomeDK supports singularity and thus can run our learning material as well. Ask us if you want to help the apps out of uCloud. Instructions will soon be available within our HPC access instructions."
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-    "section": "Try out our transcriptomics module",
-    "text": "Try out our transcriptomics module\nSo our Sandbox data scientists have finished their intro at the workshop? Great, now the brave ones in the audience can try out one of our apps in a live session. Today we are demoing:\n ### Transcriptomics If you’re interested in bulk or single cell RNA sequencing analysis and visualization, join Sandbox Data Scientist Samuele Soraggi from Aarhus University in testing out our Transcriptomics Sandbox app.\nFollow these instructions to try our app:\n\nClick on the button below to join the project for today: &lt;!DOCTYPE html&gt;\n\n\n\n\n\n&lt;p&gt;Green Button&lt;/p&gt;\n\n\n\n\n\nGo to Link\n\n\nYou should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course.\nBe sure you have joined the project. Check if you have the project OMICS workshop from the project menu (red circle). Afterwards, click on the App menu (green circle) \n\nFind the app Transcriptomics Sandbox (red circle), which is under the title Featured.\n\n\n\n\nClick on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2; choose at least 3 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs). Choose the course RNAseq in RStudio from the drop-down menu (Nr 4). Finally, click on the blue button Add Folder.\n\n\n\nNow, click on the browsing bar that appears (red circle).\n\n\n\nIn the appearing window, you should see already a folder called Intro_to_scRNAseq_R. Click on Use at its right (red circle)\n\n\n\nAfterwards, you should have something like this in the settings page:\n\n\n\nNow, click on Submit to start the app (the button is on the right side of the settings page)\nYou will now enter a waiting queue. When the session starts, the timer begins to count down (red circle), and you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle)\n\n\n\nOpen the interface by clicking on the button (green circle of figure above). Sometimes you are warned of a missing connection: simply refresh the page. You will enter Rstudio, well-known interface to code in R.\nRun the following command to download the tutorial: download.file(\"https://raw.githubusercontent.com/hds-sandbox/ELIXIR-workshop/main/Notebooks/scRNAseq_Tutorial_R.Rmd\", \"tutorial_scrna.Rmd\")\nOpen the file tutorial_scrnaR.Rmd that should now appear in the file browser of Rstudio. Click now on visual (on the tool bar) if you need to see the tutorial in a more readable format.\nThe executable code is inside chunks (called cells) to be executed in order from the first to the last using the little green arrow appearing on the right side of each code cell.\nRead carefully through the tutorial and execute the code cells. You will see the outputs appearing as you proceed."
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+    "text": "Our OMICS apps\nThe agenda starts with an introduction to High Performance Computing (HPC) and uCloud. You will try two apps during the workshop, but we are developing others, and have deployed three apps already.\n \n\n\n\nProteomics Sandbox: Our sandbox modern with a suite of proteomics analysis tools, used for example in clinical proteomics. This app is not alone, since our data scientist Jacob has also made the app ColabFold on UCloud, with methods for protein structure prediction.\n\n\n \n\n\n\nTranscriptomics Sandbox : Our sandbox for bulk or single-cell RNA sequencing analysis and visualization - amongst others two regular workshops and provides stand-alone visualization tools. In the next update, we will introduce advanced tutorials for more complex single-cell RNA sequencing analysis from some of our supported courses.\n\n\n \n\n\n\nGenomics Sandbox: Our sandbox NGS data analysis and applications range from genome assembly to variant calling to metagenomics. We have currently a semester-long population genomics course and an NGS course with many applications (alignment, VCF analysis, bulk-RNA data, single-cell RNA sequencing)"
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-    "text": "Discussion and feedback\nWe hope you enjoyed the live demo. If you have broader questions, suggestions, or concerns, now is the time to raise them! If you are totally toast for the day, remember that you can check out longer versions of our tutorials as well as other topics and tools in each of the Sandbox modules or join us for a multi-day in person course.\nAs data scientists, we also would be really happy for some quantifiable info and feedback - we want to build things that the Danish health data science community is excited to use. Please answer these 5 questions for us before you head out for the day (link activated on day of the workshop).\n\nNice meeting you and we hope to see you again!"
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-    "text": "Sandbox resources have been organized as training modules focused on key topics in health data science. We are constantly adding additional resources and have plans to create additional modules on medical imaging and wearable device data. Feel free to adapt these resources for your own purposes (with credit to the National Health Data Science Sandbox project and other projects they acknowledge in the specific materials).\nYou can access our training modules through:"
+    "text": "Discussion and feedback\nWe hope you enjoyed the live demo. If you have broader questions, suggestions, or concerns, now is the time to raise them! If you are totally toast for the day, remember that you can check out longer versions of our tutorials as well as other topics and tools in each of the Sandbox modules or join us for a multi-day in-person course (follow our news here).\nAs data scientists, we also would be really happy for some quantifiable info and feedback - we want to build things that the Danish health data science community is excited to use. Please answer these 5 questions for us before you head out for the day 3.\n \n\n\n\n\n\n\n\nNice meeting you and we hope to see you again!"
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+    "text": "Footnotes\n\n\nOther institutions (e.g. hospitals, libraries, …) can log on through WAYF. See all institutions here↩︎\nTo use Sandbox materials outside of the workshop: remember that each new user has hundreds of hours of free computing credit and around 50GB of free storage, which can be used to run any uCloud software. If you run out of credit (which takes a long time) you’ll need to check with the local DeiC office at your university about how to request compute hours on UCloud. Contact us at the Sandbox if you need help or want more information.↩︎\nlink activated on day one of the workshop.↩︎"
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+    "text": "Jose Alejandro Romero Herrera :custom-orcid: :simple-github:"
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-    "text": "Proteomics\n\n\n\n\n\n\n\nProteomics is the study of proteins that are produced by an organism. Proteomics allows us to analyze protein composition and structure, which have great importance in determining their function.\nUse the Proteomics Sandbox App on UCloud to explore pre-installed tools for proteomics analysis and other resources:\n\nProteomics Sandbox Documentation (last update: May 2023)\nIntroduction to Clinical Proteomics (course under development)\n\nWe also offer a tutorial on UCloud’s ColabFold app, a tool that allows predictions with AlphaFold2 or RoseTTAFold.\n\nColabFold Intro (last update: October 2022)"
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+    "text": "Proteomics\nProteomics is the study of proteins summed across a complete sample (ranging from a single cell to a whole organism). High-throughput measurement is conducted using mass spectrometry techniques and protein arrays, and provides insight into protein expression profiles and interactions."
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-    "text": "Electronic Health Records\n\n\n\n\n\n\n\nElectronic health records (EHRs) are digital records kept in the public health sector that record the medical histories of individuals, and access is normally highly restricted to preserve patient privacy. This data is sometimes also shared (partly or in full) in secondary patient registries that support research on a specific disease or condition (such as breast cancer or cystic fibrosis). These datasets are extraordinarily valuable in the development of predictive models used in precision medicine.\nThe chronic lymphocytic leukemia synthetic dataset listed below is generated solely from public data. It is of low utility, so we don’t recommend its use beyond the course it was designed for (with much explanation for the students on its construction and caveats). Please see Synthetic Data for more information.\n\nChronic Lymphocytic Leukemia synthetic dataset created for use in “Fra realworld data til personlig medicin”, a course from the University of Copenhagen’s MS in Personlig Medicin (last update: January 2023)\nIntro to EHR analysis (workshop under development)"
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+    "text": ":fontawesome-brands-github: GitHub Repository\nUpdated: January 2021\nStatus: Under expansion\nThe general strategy for the clinical proteomics module is to provide software, computing resources, datsets and storage using UCloud. Written material (instructions etc.), example notebooks and other auxiliary files will be provided in a Github repository.\n\nProteomics Sandbox app will be used for GUI programs\n\nPrimarily for identification / quantification\nFragPipe / MSFragger for database search (and perhaps open search)\nPDV for visualizing spectral matches\nSearchGUI and PeptideShaker also available\n\nJupyterLab app for data analysis after quantification\n\nInit script to activate conda environment and install custom kernel\nNotebooks provided through Github (https://github.com/hds-sandbox/proteomics-course)\n\nDatasets, (installed) software and JSON config files stored in UCloud project folders\n\nStudents currently need to be project members\n\n\nIntended use: Self-guided introduction to basic proteomics\n!!! abstract “Syllabus” 1. Identify and quantify peptides/proteins * “Database search” using MSFragger/FragPipe or MaxQuant * Visualize peptide spectrum matches using e.g. PDV, IDPicker, IPSA, … 2. Quality control analysis 3. Bioinformatics * Reintegrate clinical metadata * JupyterLab / RStudio + e.g. PolySTest / VSClust / … 4. PhosphoProteomics\n!!! info “Workshop requirements” Knowledge of Python and Jupyter Notebooks\n\n\n\nBMB online computational proteomics course\nNordBioNet summer school 2021 (workshops)\nIntroduction to bioinformatics for proteomics - Prof. Harald Barsnes, University of Bergen\nQC workshop and Quantitative Analysis workshop, long 2019 version - Prof. Veit Schwammle, University of Southern Denmark\nSimulation of proteomics data - Dr. Marie Locard-Paulet, University of Copenhagen\nProteogenomics - Dr. Marc Vaudel, University of Bergen\n\n\n\n\nCenter for Health Data Science, University of Copenhagen."
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-    "text": "Genomics\nGenomics is the study of genomes, the complete set of an organism’s DNA. Genomics research now encompasses functional and structural studies, epigenomics, and metagenomics, and genomic medicine is under active implementation and extension in the health sector.\nModules linked to genomics topics are currently under construction."
+    "text": "BMB online computational proteomics course\nNordBioNet summer school 2021 (workshops)\nIntroduction to bioinformatics for proteomics - Prof. Harald Barsnes, University of Bergen\nQC workshop and Quantitative Analysis workshop, long 2019 version - Prof. Veit Schwammle, University of Southern Denmark\nSimulation of proteomics data - Dr. Marie Locard-Paulet, University of Copenhagen\nProteogenomics - Dr. Marc Vaudel, University of Bergen\n\n\n\n\nCenter for Health Data Science, University of Copenhagen."
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-    "text": "AlphaFold\n:fontawesome-brands-github: GitHub Repository\nUpdated: January 2022\nStatus: Under expansion\nThis module will contain a basic standalone tutorial on how to run the newly implemented AlphaFold app in the Sandbox (UCloud version).\nIntended use: The aim of this repository is to on-board users for AlphaFold on Computerome/UCloud.\n!!! abstract “Syllabus” 1. Introduction to protein structural analysis 2. Evaluating predicted structures (AlphaFold DB) 3. Using the AlphaFold app to predict new structures (AlphaFold) 4. Replicating an AlphaFold study 5. Future developments possible with AlphaFold\n!!! info “Workshop requirements” Knowledge of Python and Jupyter notebooks\n\nAcknowledgements\n\nCenter for Health Data Science, University of Copenhagen."
+    "text": "Electronic Health Records\nElectronic health records (EHRs) are digital records kept in the public health sector that record the medical histories of individuals, and access is normally highly restricted to preserve patient privacy. This data is sometimes also shared (partly or in full) in secondary patient registries that support research of a specific disease or condition (such as cystic fibrosis). These datasets are extraordinarily valuable in the development of predictive models used in precision medicine.\nModules linked to EHR analysis are currently under development."
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-    "text": ":material-web-plus: Course Page\n\nThis workshop material includes a tutorial on how to approach RNAseq data, starting from your count matrix. Thus, the workshop only briefly touches upon laboratory protocols, library preparation, and experimental design of RNA sequencing experiments, mainly for the purpose of outlining considerations in the downstream bioinformatic analysis. This workshop is based on the materials developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC), a collection of modified tutorials from the DESeq2 and R language vignettes.\nIntended use: The aim of this repository is to run a comprehensive but introductory workshop on bulk-RNAseq bioinformatic analyses. Each of the modules of this workshop is accompanied by a powerpoint slideshow explaining the steps and the theory behind a typical bioinformatics analysis (ideally with a teacher). Many of the slides are annotated with extra information and/or point to original sources for extra reading material.\n\n\nBy the end of this workshop, you should be able to analyse your own bulk RNAseq count matrix:\n\nNormalize your data.\nExplore your samples with PCAs and heatmaps.\nPerform Differential Expression Analysis.\nAnnotate your results.\n\n!!! agenda “Syllabus” 1. Course Introduction 2. Setup 3. Experimental planning 4. Data Explanation 5. Preprocessing 6. RNAseq counts 7. Exploratory analysis 8. Differential Expression Analysis 9. Functional Analysis 10. Summarized workflow\n!!! info “Workshop prerequisites” - Knowledge of R, Rstudio and Rmarkdown. It is recommended that you have at least followed our workshop R basics - Basic knowledge of RNAseq technology - Basic knowledge of data science and statistics such as PCA, clustering and statistical testing\n\n\n\nCenter for Health Data Science, University of Copenhagen.\nHugo Tavares, Bioinformatics Training Facility, University of Cambridge.\nSilvia Raineri, Center for Stem Cell Medicine (reNew), University of Copenhagen.\nHarvard Chan Bioinformatics Core (HBC), check out their github repo"
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-    "text": "By the end of this workshop, you should be able to analyse your own bulk RNAseq count matrix:\n\nNormalize your data.\nExplore your samples with PCAs and heatmaps.\nPerform Differential Expression Analysis.\nAnnotate your results.\n\n!!! agenda “Syllabus” 1. Course Introduction 2. Setup 3. Experimental planning 4. Data Explanation 5. Preprocessing 6. RNAseq counts 7. Exploratory analysis 8. Differential Expression Analysis 9. Functional Analysis 10. Summarized workflow\n!!! info “Workshop prerequisites” - Knowledge of R, Rstudio and Rmarkdown. It is recommended that you have at least followed our workshop R basics - Basic knowledge of RNAseq technology - Basic knowledge of data science and statistics such as PCA, clustering and statistical testing\n\n\n\nCenter for Health Data Science, University of Copenhagen.\nHugo Tavares, Bioinformatics Training Facility, University of Cambridge.\nSilvia Raineri, Center for Stem Cell Medicine (reNew), University of Copenhagen.\nHarvard Chan Bioinformatics Core (HBC), check out their github repo"
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+    "text": "Introduction\n\nLook at this!\n\nLorem markdownum voluntas et praeteritae aliquando Cauno thyrso inevitabile est, interdum fingit educat, aliquo ungues solito sermo. Miscent pulveris me fletus moenia sed simul aequoris removit, te incursu.\n\nIncursurus liquidis Troiae\nEn ad staminaque ullam\nPellis erat collo Hectoreis cadavera quia draconem"
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-    "text": "Transcriptomics\nTranscriptomics is the study of RNA transcripts and provides insight into gene expression patterns. State-of-the-art approaches rely on high-throughput sequencing of transcripts sampled by various methods."
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+    "text": "Agenda\n!!! note “In this tutorial, we will cover:” * First item * Second item"
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-    "text": "Accessing the Sandbox on Computerome\nWe do not currently support independent use of Sandbox materials on Computerome. Access is supported via courses collaborating with the Sandbox and run on Computerome’s Course Platform. Check here for more info.\nThe below instructions are provided as reference for course participants.\nTo set up a user account on Computerome, you will need to provide administrators with your name, email address, and phone number for two-factor authentication. Once approved as a user, you will receive your username and server address (URL) by email, and you will receive an initial course-platform password by text.\nOn Computerome, the Sandbox environment is deployed as a virtual machine with a Linux desktop as user interface. This environment can be accessed through VMware Horizon using two different methods: (A) a desktop client (which you install on your computer) or (B) a web-based client (for those without install privileges on their computer). Please follow the appropriate instructions (A versus B) depending on your access preference.\n!!! note “Sign-In Instructions” 1. On FIRST login, enter the provided server address (URL) in a browser window to access the environment using your provided credentials. The URL will take you to a VMware Horizon access portal where you can * (A) choose to install the desktop client (left: ‘Install VMware Horizon Client’). You will then open this client for all subsequent logins instead of using the server address, and can login starting from step 2. * (B) access the environment via browser (right: ‘VMware Horizon HTML Access’). You will always use the server address in your browswer to access this entry point if this is your chosen method of access.\n2. Select the cloud icon\n  *  (A) which is linked to the server URL. This option appears when you have successfully installed and opened the VMware Horizon client.\n  *  (B) which is linked to the Sandbox course. This option appears after you have selected VMware Horizon HTML Access.\n\n3. Enter your username and your course-platform password. \n  *  On the first sign-in, this will be the course-platform password texted to you. You will then be prompted to create your own permanent password to replace this password which you will use for all future sign-ins.\n\n4. When prompted, enter the one-time password texted to you from DTU (NOT the same password as the course-platform password).\n  *  (A) If it is your first login / you logged off at last access, press any key when greeted with the blue time status screen. This will allow you to select your own user account in a dialog box.\n\n5. Sign-in using your course-platform password again after choosing the correct language for the environment in the upper right corner of the screen (this is important for the keyboard and typing your password). Danish (the da option) is default, so those with English keyboards will need to switch to English (the en option) at every login.\n\n6. Congratulations, you have entered the Sandbox environment. Relevant links for courses should be present on your desktop.\n!!! warning “Exit Instructions” To exit the environment, you have two options with different outcomes. You can log off and kill all running processes, or you can disconnect and your processes will continue running. “Power off” is disabled for users as this will shut down your virtual machine, local settings and user files may be lost, and the virtual machine will need to be manually restarted for your account.\n1. To exit and kill all running processes, select the power icon in the upper right corner, then select your name and choose \"log off\" in the pop up window.\n2. To exit and preserve running processes,\n    *  (A) hover at the top of the screen for a few seconds until your VMware Client menu is accessible, choose \"Connection\", and select \"Disconnect\".\n    *  (B) close the browser tab where you are accessing the Sandbox environment."
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+    "text": "Section 1\nLorem markdownum voluntas et praeteritae aliquando Cauno thyrso inevitabile est, interdum fingit educat, aliquo ungues solito sermo. Miscent pulveris me fletus moenia sed simul aequoris removit, te incursu.\n??? question “How does this thing work?”\nBy looking at it long enough!"
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-    "text": "High-Performance Computing (HPC) is crucial for researchers because it provides the computational power necessary to tackle complex and data-intensive problems. HPC systems, with their advanced processing capabilities, allow researchers to perform tasks that would be impractical or impossible with standard computers. UCloud is one such example, designed to be user-friendly with an intuitive graphical interface. It is flexible and extensible to handle multi-scale, multi-disciplinary research challenges, making complex digital technology accessible to all users.\nUser accounts on UCloud are enabled by university login credentials using WAYF (Where Are You From). Access the WAYF login portal here, and then find your affiliated Danish university using the search bar. After login, we suggest setting up Two Factor Authentication by clicking on the icon in the top-right corner of the screen. Once you are an approved user of UCloud, you can access the Sandbox environment via different ‘Sandbox’ apps linked to topical modules that you deploy using your own storage and computing resources - just go to Apps once you have signed into UCloud and search ‘Sandbox’ to find what we have deployed. Each app page has its own Documentation link that will direct you to Sandbox-based usage guidelines which may be customized to the app’s particular tools and scope. Apps will feature various modules that you can select initially, creating a personal copy of the training materials in your workspace for you to edit.\nEach Danish university has its usage relationship with UCloud as governed by their local front office of DeiC - check with your university IT support / DeiC representatives about requesting computational resources. For example, the University of Copenhagen has previously allotted an initial chunk of free UCloud compute hours to staff (from PhD students to professors as well as non-academic staff). If you have further questions about getting compute resources, please contact Sandbox staff.\nExtensive documentation on the general use of UCloud (how to use apps and run jobs, etc.) is available in the UCloud user guide.\n\n\n\n\n\n\nTip\n\n\n\nClick on the images to view them in full size.\n\n\n\n\n\n\nLog onto UCloud at the address http://cloud.sdu.dk using university credentials.\n\n\n\nWhen you are logged in, choose the project from the dashboard (top-right side) from which you would like to utilize compute resources. Every user has their personal workspace (My workspace). You can also provision your own project (check with your local DeiC office if you’re new to UCloud) or you can be invited to someone else’s project. If you’ve previously selected a project, it will be launched by default. If it’s your first time, you’ll be in your workspace. If you’ve joined one of our courses or workshops, your instructor will let you know which to choose.\nFor this example, we select Sandbox RNAseq workshop.\n\n\n\nDashboard: your workspace\n\n\nOn the left side, you can see the structure of the project (content changes when you select a different project):\n\nFiles: all folders/files you have access to. You can navigate through folders, download, upload, or share files with collaborators. You might have varying rights across folders, mostly depending on whether they are yours or have been shared with you\nProject: details\nResources: allocated to your workspace or a project (shared)\napplications: gain access to the apps catalog on ucloud. We refer to apps as the software applications that can be deployed on the cloud. It’s recommended to explore the featured ones. Use the search bar to find the sandbox apps\nRuns: from where you submit your jobs and past runs information\n\n\n\n\n\n\n\nImportant\n\n\n\nDon’t forget to accept the invitation to access new projects. Remember to switch projects to access other files and resources. Test switching among projects and observe how the dashboard changes.\n\n\nAt the bottom left corner, you will find your user ID, which you may need to provide once the course starts or for future collaborations, such as being added to other people’s projects. You can also find it on UCloud docs.\n\n\n\nDashboard: bottom-left menu\n\n\nIn the dashboard, you will also find news and UCloud releases, recent runs, resources allocations, and other notifications between other applications:\n\nResource allocations: indicate your currently allocated resources (e.g., KU employees have access to 1000kr in computing).\nGrant applications: apply for more resources (computing or storage if you run out of them)\n\n\n\n\nThen click on Apps in the left panel to investigate what tools and environments you can use (orange square). The easiest way to find Sandbox resources is to search via the toolbar (red circle). In this example, we’ll select the Genomics Sandbox (which will bring you to the submission screen).\n\n\n\nDashboard: all apps\n\n\n\n\n\n\n\n\nTip\n\n\n\nMark them as favorites so they appear on your dashboard.\n\n\n\n\n\nClick on the app button to get into the settings window. First, we recommend reading the documentation of the app (step 2). Then, you can configure the app as shown below, or be provided with a configuration file made available in a workshop’s project folders (import parameters) which will take care of everything for you.\n\n\n\nDashboard jobs: configuration step\n\n\nIn this example, we configure our session by:\n\nName and version of the app to run\nRead the documentation before using any app\nImport parameters (from previous runs or JSON files tailored for the app)\nJob settings: enter a job name (descriptive of the task), select the time (in hours) we want to use a node for (it can be modified afterwards!) and the machine type (selecting a 4 CPU standard node with 24 GB memory)\nOptional: add folders to access while in this job (e.g.: /home)\nChoose the module in the app you want to run (some apps have several modules that load different notebooks and data)\nClick on the Submit button (and wait!)\n\n\n\n\n\n\n\nImportant\n\n\n\nStep 4 sets up our computing resources for the period we want to work and can be customized as needed. However, only the time can be modified after submitting the job. For some of the Sandbox apps, you might want to select folders (Home and the Notebooks/Data from the module to avoid downloading it every time you start a new job). If you are in doubt, read the documentation specific to the app you are interested in.\nSelect the version of the app (if in doubt, use the latest one). This allows you to run specific versions of software.\n\n\nThere are different types of apps, and therefore, interfaces. Some, like RStudio or Jupyter Notebooks, have their own graphical user interface, whereas others are command-line interfaces. Lastly, you can also deploy a virtual desktop and virtual machine, which allow you to spin up a virtual computer.\n\n\n\nWait to go through the queue. When the session starts, the timer begins to count down. In a couple of minutes, you should be able to open the interface through the button (Open interface) in a new window (refresh the window if needed).\n\n\n\nDashboard jobs: running the app\n\n\nThis page will remain open while you work (or you can return to it via Runs in the left panel). You can end your session early by pressing and holding Stop application (red button), you can see how much time you have left and can add hours to your session as you go (blue buttons in orange square).\n\n\n\nDifferent apps might employ distinct development environments, so your interface experience could vary accordingly. If you’re utilizing an RStudio-based application, like the transcriptomics tool, your interface will launch in a new tab, resembling the image provided below. Simply navigate through the folders to locate and access the R Markdown notebooks.\n\n\n\nRStudio interface: running the app\n\n\nIf you are testing a JupyterLab-based application, such as the genomic app, your interface should look like in the image below. In this case, you will be working from JupyterLab. You can open Jupyter Notebooks (yellow square), R studio (blue square) or a terminal (black square) among others. In this case, the highlighted buttons (under Notebooks) have all the software and packages that you will need pre-installed (this is not the case with Python 3 to the left).\n\n\n\nJupyterLab interface: running the app\n\n\nYou can navigate through the different folders and start running the Python notebooks (orange square).\n\n\n\nJupyterLab interface: opening notebook\n\n\nIf you are an advanced user, you can also create your own Python files and select the Kernel NGS (python) to use the pre-installed software. Learn how to manage (upload and download new data) and share files that you have created/developed with collaborators here.\n\n\n\n\n\n\nTip\n\n\n\nCreate your own directories to save the output of your jobs. You will be able to access them later in your project folders under the resources you are using\nIf you haven’t created any directories, look for the generated files under a folder with the same name as the job name you used.\n\n\nYou are ready to start using Ucloud and the sandbox tools!"
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-    "text": "Log onto UCloud at the address http://cloud.sdu.dk using university credentials.\n\n\n\nWhen you are logged in, choose the project from the dashboard (top-right side) from which you would like to utilize compute resources. Every user has their personal workspace (My workspace). You can also provision your own project (check with your local DeiC office if you’re new to UCloud) or you can be invited to someone else’s project. If you’ve previously selected a project, it will be launched by default. If it’s your first time, you’ll be in your workspace. If you’ve joined one of our courses or workshops, your instructor will let you know which to choose.\nFor this example, we select Sandbox RNAseq workshop.\n\n\n\nDashboard: your workspace\n\n\nOn the left side, you can see the structure of the project (content changes when you select a different project):\n\nFiles: all folders/files you have access to. You can navigate through folders, download, upload, or share files with collaborators. You might have varying rights across folders, mostly depending on whether they are yours or have been shared with you\nProject: details\nResources: allocated to your workspace or a project (shared)\napplications: gain access to the apps catalog on ucloud. We refer to apps as the software applications that can be deployed on the cloud. It’s recommended to explore the featured ones. Use the search bar to find the sandbox apps\nRuns: from where you submit your jobs and past runs information\n\n\n\n\n\n\n\nImportant\n\n\n\nDon’t forget to accept the invitation to access new projects. Remember to switch projects to access other files and resources. Test switching among projects and observe how the dashboard changes.\n\n\nAt the bottom left corner, you will find your user ID, which you may need to provide once the course starts or for future collaborations, such as being added to other people’s projects. You can also find it on UCloud docs.\n\n\n\nDashboard: bottom-left menu\n\n\nIn the dashboard, you will also find news and UCloud releases, recent runs, resources allocations, and other notifications between other applications:\n\nResource allocations: indicate your currently allocated resources (e.g., KU employees have access to 1000kr in computing).\nGrant applications: apply for more resources (computing or storage if you run out of them)\n\n\n\n\nThen click on Apps in the left panel to investigate what tools and environments you can use (orange square). The easiest way to find Sandbox resources is to search via the toolbar (red circle). In this example, we’ll select the Genomics Sandbox (which will bring you to the submission screen).\n\n\n\nDashboard: all apps\n\n\n\n\n\n\n\n\nTip\n\n\n\nMark them as favorites so they appear on your dashboard.\n\n\n\n\n\nClick on the app button to get into the settings window. First, we recommend reading the documentation of the app (step 2). Then, you can configure the app as shown below, or be provided with a configuration file made available in a workshop’s project folders (import parameters) which will take care of everything for you.\n\n\n\nDashboard jobs: configuration step\n\n\nIn this example, we configure our session by:\n\nName and version of the app to run\nRead the documentation before using any app\nImport parameters (from previous runs or JSON files tailored for the app)\nJob settings: enter a job name (descriptive of the task), select the time (in hours) we want to use a node for (it can be modified afterwards!) and the machine type (selecting a 4 CPU standard node with 24 GB memory)\nOptional: add folders to access while in this job (e.g.: /home)\nChoose the module in the app you want to run (some apps have several modules that load different notebooks and data)\nClick on the Submit button (and wait!)\n\n\n\n\n\n\n\nImportant\n\n\n\nStep 4 sets up our computing resources for the period we want to work and can be customized as needed. However, only the time can be modified after submitting the job. For some of the Sandbox apps, you might want to select folders (Home and the Notebooks/Data from the module to avoid downloading it every time you start a new job). If you are in doubt, read the documentation specific to the app you are interested in.\nSelect the version of the app (if in doubt, use the latest one). This allows you to run specific versions of software.\n\n\nThere are different types of apps, and therefore, interfaces. Some, like RStudio or Jupyter Notebooks, have their own graphical user interface, whereas others are command-line interfaces. Lastly, you can also deploy a virtual desktop and virtual machine, which allow you to spin up a virtual computer.\n\n\n\nWait to go through the queue. When the session starts, the timer begins to count down. In a couple of minutes, you should be able to open the interface through the button (Open interface) in a new window (refresh the window if needed).\n\n\n\nDashboard jobs: running the app\n\n\nThis page will remain open while you work (or you can return to it via Runs in the left panel). You can end your session early by pressing and holding Stop application (red button), you can see how much time you have left and can add hours to your session as you go (blue buttons in orange square).\n\n\n\nDifferent apps might employ distinct development environments, so your interface experience could vary accordingly. If you’re utilizing an RStudio-based application, like the transcriptomics tool, your interface will launch in a new tab, resembling the image provided below. Simply navigate through the folders to locate and access the R Markdown notebooks.\n\n\n\nRStudio interface: running the app\n\n\nIf you are testing a JupyterLab-based application, such as the genomic app, your interface should look like in the image below. In this case, you will be working from JupyterLab. You can open Jupyter Notebooks (yellow square), R studio (blue square) or a terminal (black square) among others. In this case, the highlighted buttons (under Notebooks) have all the software and packages that you will need pre-installed (this is not the case with Python 3 to the left).\n\n\n\nJupyterLab interface: running the app\n\n\nYou can navigate through the different folders and start running the Python notebooks (orange square).\n\n\n\nJupyterLab interface: opening notebook\n\n\nIf you are an advanced user, you can also create your own Python files and select the Kernel NGS (python) to use the pre-installed software. Learn how to manage (upload and download new data) and share files that you have created/developed with collaborators here.\n\n\n\n\n\n\nTip\n\n\n\nCreate your own directories to save the output of your jobs. You will be able to access them later in your project folders under the resources you are using\nIf you haven’t created any directories, look for the generated files under a folder with the same name as the job name you used.\n\n\nYou are ready to start using Ucloud and the sandbox tools!"
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-    "text": "Welcome to the Health Data Science Sandbox\n\nAccess our training modules\n\n\n\n\n\n\n\n HPC Lab\n\nHPC launch\nHPC pipes\nRDM\n\n\n\n Genomics\n\nNGS data analysis \nPopulation Genomics\nGWAS\n\n\n\n Transcriptomics\n\nBulk RNAseq\nSingle-cell RNAseq\n\n\n\n Proteomics\n\nClinical Proteomics\nColabFold \n\n\n\n Health records\n\nSynthetic data \nPersonalized Medicine \n\n\n\n\n\nWe are a collaborative project with team members spanning five Danish universities\n\nThe Health Data Science Sandbox is a national project coordinated by the Center for Health Data Science at the University of Copenhagen. We’re working with a network of health data science experts to build training resources on academic supercomputers for students and researchers in Denmark. Our Sandbox contains training modules that pair topical datasets with recommended analysis tools, pipelines, and learning materials/tutorials in a portable, containerized format.\n\n\n\n\n\n\nTo get involved as a trainee, researcher, or educator in Denmark:\nTRAINEES: join our next scheduled workshop or a supported university course\nTRAINEES/RESEARCHERS: explore training modules independently on UCloud\nRESEARCHERS: adapt training modules or code repositories to your research\nEDUCATORS: host a training event or course in the Sandbox with our support\n\n \n\n\n\n\n\n\nA note on Sandbox data policy\n\n\n\nThe Sandbox aims to be a resource for learning new analysis approaches and tools for health data science on useful, interesting, and safe-to-share datasets. All person-specific datasets in the Sandbox are non-sensitive and GDPR-safe because they are 1) sourced from public databases, 2) fully anonymous/non-sensitive from a GDPR perspective, and/or 3) synthetic. To learn more, check out Datasets where we explain our data policy in detail and our approach to synthetic data generation.\n\n\nThanks to the Novo Nordisk Foundation for funding the National Health Data Science Project! Please give credit if you use our open-source materials in any form (NNF grant number NNF20OC0063268)."
+    "text": "The Sandbox is collaborating with the two major academic high performance computing platforms in Denmark. Computerome is located at the Technical University of Denmark (and co-owned by the University of Copenhagen) while UCloud is owned by the University of Southern Denmark. These HPC platforms each have their own strengths which we leverage in the Sandbox in different ways."
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+    "text": "UCloud\nUCloud is a relatively new HPC platform that can be accessed by students at Danish universities (via a WAYF university login). It has a user friendly graphical user interface that supports straightforward project, user, and resource management. UCloud provides access to many tools via selectable Apps matched with a range of flexible compute resources. Check out UCloud’s extensive user docs here.\nThe Sandbox is deploying training modules in this form such that any UCloud user can easily access Sandbox materials independently. The Sandbox is also hosting workshops and training events on UCloud in conjunction with in-person training. Click below for detailed instructions on accessing Sandbox apps.\n \n\n UCloud Guide for Sandbox apps"
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+    "text": "Computerome\nComputerome is the home of many sensitive health datasets via collaborations between DTU, KU, Rigshospitalet, and other major health sector players in the Capital Region of Denmark. Computerome has recently launched their secure cloud platform, DELPHI, and in collaboration with the Sandbox has built a Course Platform on the same backbone such that courses and training can be conducted in the same environment as real research would be performed in the secure cloud. The Sandbox is supporting courses in the Course Platform, but it is also available for independent use by educators at Danish universities. Please see their website for more information on independent use and pricing, and contact us if you’d like to collaborate on hosting a course on Computerome. We can help with tool installation, environment testing, and user support (ranging from using the environment to course content if we have Sandbox staff with matching expertise).\nParticipants in courses co-hosted by the Sandbox can check here for access instructions."
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-    "text": "An infrastructure project for health data science training and research in Denmark\nThe National Health Data Science Sandbox project kicked off in 2021 with 5 years of funding via the Data Science Research Infrastructure initiative from the Novo Nordisk Foundation. Health data science experts at five Danish universities are contributing to the Sandbox with coordination from the Center for Health Data Science under lead PI Professor Anders Krogh. Data scientists hosted in the research groups of each PI are building infrastructure and training modules on Computerome and UCloud, the primary academic high performance computing (HPC) platforms in Denmark. If you have any questions or would like to get in touch with one of our data scientists, please contact us here.\n\n\n\n\n\nOur computational ‘sandbox’ allows data scientists to explore datasets, tools and analysis pipelines in the same high performance computing environments where real research projects are conducted. Rather than a single, hefty environment, we’re deploying modularized topical environments tailored for independent use on each HPC platform. We aim to support three key user groups based at Danish universities:\n\ntrainees: use our training modules to learn analysis techniques with some guidance and guardrails - for your data type of interest AND for general good practices for HPC environments\n\nresearchers: prototype your tools and algorithms with an array of good quality datasets that are GDPR compliant and free to access\neducators: develop your next course with computational assignments in the HPC environment your students will use for their research\n\nActivity developing independent training modules and hosting workshops has centered on UCloud, while collaborative construction of a flexible Course Platform has been completed on Computerome for use by the Sandbox and independent educators. Publicly sourced datasets are being used in training modules on UCloud, while generation of synthetic data is an ongoing project at Computerome. Sandbox resources are under active construction, so check out our other pages for the current status on HPC Access, Datasets, and Modules. We run workshops using completed training modules on a regular basis and provide active support for Sandbox-hosted courses through a slack workspace. See our Contact page for more information.\n\n\nPartner with the Sandbox\nThe Sandbox welcomes proposals for new courses, modules, and prototyping projects from researchers and educators. We’d like to partner with lecturers engaged with us in developing needed materials collaboratively - we would love to have input from subject experts or help promote exciting new tools and analysis methods via modules! Please contact us with your ideas at nhds_sandbox@sund.ku.dk.\n\nWe thank the Novo Nordisk Foundation for funding support. If you use the Sandbox for research or reference it in text or presentations, please acknowledge the Health Data Science Sandbox project and its funder the Novo Nordisk Foundation (grant number NNF20OC0063268)."
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-    "text": "A priority of the Sandbox is to guide health data science learning using real-world-similar datasets. A major component is addressing how to analyze and leverage person-specific data, such as electronic health records, without invading personal privacy or straying from GDPR guidelines on sensitive data use. We are therefore focused on using either publicly accessible datasets (that are generally well anonymized to enable such release) or we are using/creating synthetic datasets that mimic real-world datasets without replicating real people’s data such that they can be identified. In either case, it is essential for Sandbox users to treat person-specific data respectfully and be aware of the additional responsibility and limitations of working with this type of data as part of their career in health data science.\nWe recommend that users interested in this type of data complete an ethics course on research using health datasets before digging into any analysis. A well regarded course that is also often required for using public databases that contain person-specific data is the Human Subject and Data Research Ethics course designed by the Massachusetts Institute of Technology. The course is hosted at CITI, the Collaborative Institutional Training Initiative. Completing the course is free of charge and provides you with a certificate which you may need to upload to certain databases to gain access. Set up an account at CITI, add an Institutional affiliation with ‘Massachusetts Institute of Technology Affiliates’, and then find and complete the course titled ‘Data or Specimens Only Research’ to obtain a certificate (in pdf form)."
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-    "text": "Public domain data\nThe intended scope of the Sandbox is broad, and we will be pulling from many different public access databases (especially for training modules on omics analysis). Databases can be topically broad, giant repositories or field-specific, and each may have its own usage rules. We plan to provide our own copies of publically available datasets where allowed to ensure compatibility with the linked module is preserved, but some datasets may need to be downloaded by users themselves under specific access / distribution restrictions. Many omics datasets do not present significant data sensitivity concerns in comparison to real-world data such as electronic health records (EHRs) and clinical trial datasets.\nThere are large public de-identified EHR datasets that serve as benchmark resources for teaching and comparing new methods with old, but these are not numerous and often have restricted usage and sharing terms in addition to being quite dated. Historical approaches to dataset anonymization and de-identification have been substantially challenged in the age of digitalized healthcare and increasing data integration, which means meaningfully large ‘anonymized’ datasets are now rarely released."
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+    "text": "The Health Data Science Sandbox is coordinated by the Center for Health Data Science at the University of Copenhagen (KU). Sandbox data scientists are also placed in collaborating groups at the Technical University of Denmark (DTU), University of Southern Denmark (SDU), Aarhus University (AU), and Aalborg University (AAU).\nTo get in touch with the Sandbox or be connected with Sandbox staff at your university, please email us. To obtain module material for use in your own compute environment, see our GitHub organization page at hds-sandbox.\n\n\n\n\n\n\n\n\n\nMember\nRole\nInstitution\nPI\n\n\n\n\nJennifer Bartell\nProject Manager, Data Scientist\nCenter for Health Data Science, KU\nAnders Krogh\n\n\nAlba Refoyo Martinez\nData Scientist\nCenter for Health Data Science, KU\nAnders Krogh\n\n\nJakob Skelmose\nData Scientist\nDepartment of Clinical Medicine, AAU\nMartin Boegsted\n\n\nSamuele Soraggi\nData Scientist\nBioinformatics Research Centre, AU\nMikkel Schierup\n\n\nJesper Roy Christiansen\nData Scientist\nComputerome, DTU\nPeter Løngreen\n\n\nJacob Fredegaard Hansen\nData Scientist\nDepartment of Biochemistry and Molecular Biology, SDU\nOle Nørregaard Jensen &  Veit Schvämmle\n\n\n\n\nWe appreciate the contributions of previous team members José Alejandro Romero Herrera (KU), Conor O’Hare (KU), Sander Boisen Valentin (AAU) and Peter Husen (SDU).\nFind all the team members and their contacts below. Click on their names for more information about each member of the team:\n\n\n\n\n\n\n\n\n\n\n\n\nAlba Refoyo Martinez\n\n\nData Scientist, Copenhagen University\n\n\n\n\n\n\n\n\n\n\n\nJacob Fredegaard Hansen\n\n\nData Scientist, University of Southern Denmark\n\n\n\n\n\n\n\n\n\n\n\nJakob Skelmose\n\n\nData Scientist, Aalborg University\n\n\n\n\n\n\n\n\n\n\n\nJennifer Bartell\n\n\nData Scientist and Project coordinator, Copenhagen University\n\n\n\n\n\n\n\n\n\n\n\nSamuele Soraggi\n\n\nData Scientist, Aarhus University\n\n\n\n\n\n\n\nNo matching items"
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-    "text": "Synthetic data\n\n\n\n\n\n\nVia our collaborators and broader network, the Sandbox has the opportunity to simulate/synthesize data resembling different databases and registries from the Danish health sector. We are exploring methods of creating useful synthetic datasets with national and EU-level data access policies and GDPR restrictions in mind, while developing initial datasets using publicly available data from Danish research studies and other resources.\nUltimately, a new era of synthetic data is rapidly developing. The funded Sandbox proposal focused on generating synthetic data using mechanistic models, agent-based models, or draws from multivariate distributions (such as copulas), which are methods that do not present any significant GDPR-related concerns with sharing the produced datasets as they are derived from population-level characteristics and prior knowledge. However, new deep learning-based methods of data synthesis can theoretically learn complex, nonlinear patterns within a sensitive dataset and generate a synthetic dataset that replicates these patterns. This is a really promising approach for sharing high utility synthetic datasets, but it also elevates risk of accidentally sharing too much about the real dataset and skirting the boundaries of GDPR and ethical data handling. There is an inherent trade-off between privacy preservation and similarity of the synthetic dataset to the original dataset, with method development focused on moving closer to the ideal zone of high privacy AND high similarity. The figure at right is a rough approximation of this relationship versus current families of synthesis methods.\nPlease see Synthetic Data for more information about our approach to this technology."
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-    "text": "Sandbox data scientists routinely lead or contribute to courses and workshops at host universities in Denmark. Check out upcoming events here! All our past events are listed in the table below.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHow I learned to stop worrying and love RDM\n\n\n\nHPC\n\n\nRDM\n\n\nKU\n\n\nworkshop\n\n\n\nConference workshop\n\n\n\nAug 21, 2024\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHPC launch\n\n\n\nHPC\n\n\nRDM\n\n\nKU\n\n\ncourse\n\n\n\nClick to sign-up\n\n\n\nOct 30, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nHPC Pipes\n\n\n\nsnakemake\n\n\nconda\n\n\nKU\n\n\ncourse\n\n\n\nClick to sign-up\n\n\n\nNov 4, 2024\n\n\n\n\n\n\n\n\nNo matching items\n\n\n\n   \n     \n     \n       Order By\n       Default\n         \n          Event title\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\nEvent title\n\n\nDates\n\n\nLocation\n\n\nOrganizers\n\n\n\n\n\n\nIntro to NGS data analysis - Summer School\n\n\n01-05 July 2024\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nWorkshop: Digging into the Health Data Science Sandbox\n\n\n18-19 April 2024\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nCourse support at SDU\n\n\n9 February 2024\n\n\nSDU\n\n\nJacob Fredegaard Hansen\n\n\n\n\nDDSA PhD meetup and D3A conference\n\n\n1 February 2024\n\n\nDDSA\n\n\nJennifer Bartell\n\n\n\n\nA primer for Synthetic health data\n\n\n31 January 2024\n\n\n \n\n\nJennifer Bartell\n\n\n\n\nNNF Collaborative Data Science award news: the SE3D project!\n\n\n12 December 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nUpdates from SDU\n\n\n9 November 2023\n\n\nSDU\n\n\nJacob Fredegaard Hansen\n\n\n\n\nA course on RDS for NGS data\n\n\n7 November 2023\n\n\nDTU\n\n\nJose AR Herrera\n\n\n\n\nFrom Data Chaos to Data Harmony\n\n\n7 November 2023\n\n\nDeiC conference\n\n\nJennifer Bartell\n\n\n\n\n‘Digging into the Health Data Science Sandbox’ workshop\n\n\n7 September 2023\n\n\nAU\n\n\nJennifer Bartell\n\n\n\n\nSandbox workshop in Aarhus\n\n\n29 August 2023\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nWorkshop on bulkRNA-seq data\n\n\n19 June 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nSandbox App updates on UCloud rolled out\n\n\n31 May 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nSecond bulk RNA-seq course at the University of Copenhagen\n\n\n18 January 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nSandbox support for Spring 2023 courses\n\n\n10 January 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nSoft launch of the new Course Platform at Computerome\n\n\n8 January 2023\n\n\nKU\n\n\nJesper R Christiansen\n\n\n\n\nSandbox support for ‘Advanced Statistical Learning’\n\n\n30 November 2022\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nSandbox support within ‘Workshops in Applied Bioinformatics’ at SDU\n\n\n15 November 2022\n\n\nSDU\n\n\nJacob Fredegaard Hansen\n\n\n\n\nTranscriptomics Sandbox app launched on UCloud!\n\n\n15 November 2022\n\n\nKU\n\n\nJose AR Herrera\n\n\n\n\nGenomics Sandbox app launched on UCloud!\n\n\n6 September 2022\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nBulk RNA-seq course at University of Copenhagen\n\n\n18 August 2022\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nGenomics course at Aarhus University\n\n\n1 June 2022\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nBasics of Personalized Medicine - final wrap-up\n\n\n22 April 2022\n\n\nAAU\n\n\nJennifer Bartell\n\n\n\n\nBasics of Personalized Medicine - MSc course\n\n\n1 April 2022\n\n\nAAU\n\n\nJennifer Bartell\n\n\n\n\n\nNo matching items"
+    "text": "Alba is a Sandbox data scientist based at the University of Copenhagen. During her academic background as a PhD and Postdoc she has developed a solid expertise in large-scale genomics and pipelines development on computing clusters."
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-    "text": "A priority of the Sandbox is to guide health data science learning using real-world-similar datasets. A major component is addressing how to analyze and leverage person-specific data, such as electronic health records, without invading personal privacy or straying from GDPR guidelines on sensitive data use. We are therefore focused on using either publicly accessible datasets (that are generally well anonymized to enable such release) or we are using/creating synthetic datasets that mimic real-world datasets without replicating real people’s data such that they can be identified. In either case, it is essential for Sandbox users to treat person-specific data respectfully and be aware of the additional responsibility and limitations of working with this type of data as part of their career in health data science.\nWe recommend that users interested in this type of data complete an ethics course on research using health datasets before digging into any analysis. A well regarded course that is also often required for using public databases that contain person-specific data is the Human Subject and Data Research Ethics course designed by the Massachusetts Institute of Technology. The course is hosted at CITI, the Collaborative Institutional Training Initiative. Completing the course is free of charge and provides you with a certificate which you may need to upload to certain databases to gain access. Set up an account at CITI, add an Institutional affiliation with ‘Massachusetts Institute of Technology Affiliates’, and then find and complete the course titled ‘Data or Specimens Only Research’ to obtain a certificate (in pdf form).\n\n\nThe intended scope of the Sandbox is broad, and we will be pulling from many different public access databases in our development of teaching modules. There are classical datasets that serve as benchmark resources for teaching and comparing new methods with old, and also brand new datasets that will support modules on emerging technologies (such as spatial single cell RNA-seq analysis). Databases can be topically broad giant repositories or field-specific, and each may have its own usage rules. We plan to provide our own copies of publically available datasets where allowed to ensure compatibility with the linked module is preserved, but some datasets may need to be downloaded by users themselves under specific access / distribution restrictions.\n\n\n\nThe Sandbox is focused on supporting Danish health data science education and research. Via our collaborators and broader network, we have the opportunity to simulate/synthesize data resembling different databases and registries from the Danish health sector in addition to using traditional data simulation techniques to replicate general datasets. We are exploring methods of creating useful synthetic datasets with local access guidelines/GDPR restrictions in mind, while developing initial datasets using published data from Danish studies and publically available resources."
+    "text": "News and Events\n\nSandbox data scientists routinely lead or contribute to courses and workshops at host universities in Denmark. Check out upcoming events in the table below!\n\nUpcoming Sandbox Training Events\n\n{{ read_csv(‘../assets/upcomingcourses.csv’) }}\n\n\nNews & Updates\n\n\n07/11/2023 - Proteomics Sandbox Update: Enhanced Security and New Metabolomics Tools (U0niversity of Southern Denmark)\nThe Proteomics Sandbox Application has recently undergone a significant update, enhancing its security features to ensure safer usage for its users. In this latest iteration, the application has expanded its software suite by introducing two new tools: DIA-NN and MZmine, catering to the metabolomics field. Furthermore, the pre-existing software within the application has been refreshed and updated to the latest versions, ensuring that the Proteomics Sandbox Application remains at the cutting-edge of the field. Excitingly, this application will be actively utilized in the course “BMB831: Biostatistics in R II” at the University of Southern Denmark throughout this autumn, showcasing its relevance and applicability in academic settings.\n\n\n25/10/2023 - ‘Research Data Management for NGS Data’ with DeiC (Technical University of Denmark)\nSandbox data scientist Jose Alejandro Romero Herrera ran the first instance of a new module on research data management practices he developed specifically for NGS data. Twelve participants were hosted in conjunction with DeiC at DTU, and were exposed to tools like bash, conda, git, and cookie cutter in their quest to organize their omics data.\n\n\n07/09/2023 - ‘Digging into the Health Data Science Sandbox’ at the Danish Bioinformatics Conference 2023 (Aarhus University)\nThe full team of Sandbox data scientists hosted a 4 hour workshop at the Danish Bioinformatics conference where they gave a taster session of each of our 3 omics apps. We learned that multi-omics analysis were a substantial draw for the crowd at the DBC and are making plans to address this interest in future events.\n\n\n29/08/2023 - 3 days of Sandbox demos at Aarhus University\nSandbox data scientist Samuele Soraggi hosted a three day speed run through Sandbox apps at the Bioinformatics Research Center. The 26 participants joined for genomics, transcriptomics, and/or proteomics app demos depending on their interests. This thorough omics demo had maxed out participant sign-ups and an enthusiastic crew enjoyed the sessions alongside a bit of networking across disciplines. We plan to host more of these type of workshops given the event’s success!\n\n\n19/06/2023 - 3 days of bulk RNA-seq at the University of Copenhagen\nOur teaching team (from the Sandbox, the HeaDS DataLab, and reNEW’s genomics platform) hosted another 3 day workshop on bulk RNA-seq. The 34 participants used the updated version of the UCloud Transcriptomics App which provided the smoothest experience yet for both trainers and trainees. A new goal for the next course run is to add a student project to support independent implementation and exploration of the course content.\n\n\n31/05/2023 - Sandbox App updates on UCloud rolled out\nNew versions of the Genomics Sandbox App, the Transcriptomics Sandbox App, and the Proteomics Sandbox App have all been launched on UCloud this month! Check out the new components in the training modules such as a GWAS module in Genomics and new tools in Transcriptomics and Proteomics. Updates were also informed by the different courses supported during Spring 2023. With these courses wrapping up this month, the associated new training materials have also been included in the new versions of the apps.\n\n\n18/01/2023 - Second bulk RNA-seq course at the University of Copenhagen\nOn 18th of January we taught the second iteration of our bulk RNA-seq course to researchers (from PhD students to professors) at SUND at the University of Copenhagen. We had ~50 workshop participants joining us for three days of lectures and exercises on UCloud. This time, we introduced preprocessing theory (read QC, alignment and quantification) and the use of automated workflows using the nf-core rnaseq pipeline.\nFor those that could not enroll for this session, you can find the updated material here. We have moved the datasets and slides to a zenodo repository\nWe’d like to extend our thanks to our workshop collaborators, data scientists from the SUND DataLab at KU’s Center for Health Data Science as well as the genomics platform at the NNF Center for Stem Cell Medicine (reNEW).\n\n\n10/01/2023 - Sandbox support for Spring 2023 courses\n\nSandbox support for ‘Population Genomics’\nExercises for an MS course on Population Genomics taught by Prof. Kasper Munch at Aarhus University are being implemented on UCloud by Sandbox data scientist Samuele Soraggi. Students will explore the training materials on UCloud during the Spring 2023 semester, after which the materials will be accessible to any UCloud user via the Genomics Sandbox App.\n\n\n‘Fra real-world data til personlig medicin’ with Course Platform & Sandbox support\nThe second round of the course ‘Fra real-world data til personlig medicin’ in KU’s MS in Personlig Medicin begins in January with an introduction to CLL-TIM, a predictive model for chronic lymphocytic leukemia deployed by Prof. Carsten Niemann, an introduction by Sandbox coordinator Jennifer Bartell to the new Course Platform at Computerome built with Sandbox help for hosting courses with HPC resources, and an introduction to building predictive models using TidyModels in R by Prof. Rasmus Broendum. The course will run through April with 10 continuing education students building their own predictive models using a new and improved synthetic CLL dataset developed by Sandbox data scientist Sander Boisen Valentin. Jennifer and Rasmus are also manning the Sandbox Slack workspace to field student questions about the dataset and their model building.\n\n\nSandbox support for ‘Single-cell, Single-Molecule: The Next Level in Cell Biology’\nAn NNF-funded course, ‘Single-cell, Single-Molecule: The Next Level in Cell Biology’ combining experimental and computational approaches to RNA sequencing is starting at Aarhus University. In addition to course-responsible professor Stig Andersen and co-teachers Victoria Birkedal and Thomas Boesen, Sandbox PI Mikkel Schierup will be contributing along with Sandbox data scientist Samuele Soraggi. Samuele is adapting the Transcriptomics App material on UCloud to supply tutorials and exercises for this hefty course as well as serving as a teaching assistant. The course materials will be available to all users of the Transcriptomics Sandbox App on UCloud in the future.\n\n\n\n8/01/2023 - Soft launch of the new Course Platform at Computerome\nSandbox data scientist Jesper Roy Christiansen has been integral to the development of a new ‘Course Platform’ at Computerome, the HPC platform at the Technical University of Denmark. Built as a collaboration between the Sandbox and Computerome, the Course Platform will host its first users, students in ‘Fra real-world data til personlig medicin’, a course of KU’s MS in Personlig Medicin. Sandbox coordinator Jennifer Bartell and Sandbox PI Martin Boegsted have also been involved in testing this new system during course setup. See the above link as well as HPC Access for more details on this platform and how you can also use this new platform to host courses (with or without Sandbox involvement!).\n\n\n30/11/2022 - Sandbox support for ‘Advanced Statistical Learning’\nSandbox data scientist Samuele Soraggi spent two weeks teaching for the Fall 2023 course ‘Advanced Statistical Learning’ taught by Prof. Asger Hobolth at Aarhus University.\n\n\n15/11/2022 - Sandbox support for a workshop in the series ‘Workshops in Applied Bioinformatics’ at SDU\nSandbox data scientist Jacob Fredegaard Hansen created a tutorial on how to use ColabFold for a one day workshop as part of the ‘Workshops in Applied Bioinformatics’ series taught by Sandbox collaborator Veit Schwammle. The material is accessible on the Sandbox website (see Modules, Proteomics) for any UCloud user alongside the UCloud ColabFold App.\n\n\n10/12/2022 - Transcriptomics Sandbox app launched on UCloud!\nWe have deployed our second standalone Sandbox app on UCloud! Please see the Access page for instructions on how to find our Sandbox apps on UCloud - This one is titled ‘Transcriptomics Sandbox’ and module documentation is linked from the UCloud app page as well as here in Modules.\n\n\n06/09/2022 - Genomics Sandbox app launched on UCloud!\nWe have deployed our first standalone Sandbox app on UCloud! Please see the Access page for instructions on how to find our Sandbox apps on UCloud - this first one is titled ‘Genomics Sandbox’ and module documentation is linked from the UCloud app page as well as here in Modules.\n\n\n18/08/2022 - Bulk RNA-seq course at University of Copenhagen\nToday we began teaching our brand new bulk RNA-seq course to researchers (from PhD students to professors) at SUND at the University of Copenhagen. We had 32 workshop participants join us for two days of lectures and exercises on UCloud. We’d like to extend our thanks to our workshop collaborators, data scientists from the SUND DataLab at KU’s Center for Health Data Science as well as the genomics platform at the NNF Center for Stem Cell Medicine (reNEW).\nFor those that could not enroll for this session, you can find the relevant material here.\n\n\n01/06/2022 - Genomics course at Aarhus University\nA month-long course in Genomics taught by Professors Mikkel Schierup and Stig Andersen has started with lead supercomputing support on UCloud by Sandbox data scientist and course instructor Samuele Soraggi. Computational exercises in NGS analysis were deployed in a UCloud project for use by 47 graduate students with primarily molecular biology and clinical backgrounds and no prior supercomputing experience! Post-course update: We received many positive reviews on use of the Genomics Sandbox training materials on UCloud!\n\n\n22/04/2022 - Basics of Personalized Medicine - final wrap-up\nOur first course, Basics of Personalized Medicine, wrapped up this month with student project presentations which described their approaches to analysis of the synthetic Chronic Lymphocytic Leukemia dataset created for the course. Course reviews highlighted the helpfulness of Sandbox staff in troubleshooting R problems and the tremendous amount that students learned about predictive modeling.\n\n\n04/01/2022 - Basics of Personalized Medicine - MS in Personal Medicine program\nThe first course supported by the Sandbox is launching this month - ‘Basics of Personalized Medicine’ - where students in the new Master in Personal Medicine program at University of Copenhagen are introduced to predictive modeling using electronic health records."
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+    "text": "Many outside resources are available to support education in health data science, ranging from beginner-level introductions to R or Python (the primary languages of health data science) to other teaching resources and tutorials created at universities and life science organizations.\nWe encourage you to explore the training platform provided by ELIXIR (Europe’s distributed infrastructure for life-science data). On this platform (TeSS), you can find a registry of training materials as well as webinars, workshops, and in-person courses in bioinformatics, modelling, data management, and life science database usage among other topics."
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-    "text": "The Sandbox is focused on supporting Danish health data science education and research. Via our collaborators and broader network, we have the opportunity to simulate/synthesize data resembling different databases and registries from the Danish health sector in addition to using traditional data simulation techniques to replicate general datasets. We are exploring methods of creating useful synthetic datasets with local access guidelines/GDPR restrictions in mind, while developing initial datasets using published data from Danish studies and publically available resources."
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     "text": "We are currently focused on exploring methods and metrics by developing reproducible, well documented examples and use cases of synthetic data in partnership with other researchers, legal advisors, and data authorities. We’re relying primarily on publicly available tabular health datasets in this exploration phase, but we will also work with sensitive data in the future. Our rules aim to preserve the trust of the public in how their health data is handled by data authorities and researchers.\n\n\n\n\n\n\nSandbox Rules for Synthetic Data\n\n\n\n1. Creation of synthetic data involves processing sensitive data, and this requires obtaining project approvals from data authorities when performing this work on sensitive data. Any synthetic data work with restricted-access, sensitive data by the Sandbox will only be conducted with these approvals in place in the frame of a research project.\n2. Goals for each synthetic dataset project should be defined at project initiation: how will the synthetic dataset be used, who is the intended audience, and how might it be shared? This frame should govern every consequent decision for that dataset and be shared alongside the final dataset.\n3. Quantitative metrics for fidelity, utility, and privacy preservation should be implemented for each dataset and shared alongside the final dataset.\n4. A cost-benefit analysis should be performed after the project is completed - is any risk to privacy appropriately balanced by value of the dataset in achieving its stated aims and contributing to the public good?\n5. Data authorities with ethical and strategic stakes in who accesses the synthetic dataset should be included in decisions about how it is used and who is allowed to access it. \n6. Synthetic datasets created from person-specific sensitive data rather than population characteristics can still pose privacy risks, and any users of the dataset should be approved and registered. The Sandbox will not release any such datasets publicly and will instead work with appropriate data authorities to decide how such datasets should be governed in a responsible way."
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-    "text": "Many outside resources are available to support education in health data science, ranging from beginner-level introductions to R or Python (the primary languages of health data science) to other teaching resources and tutorials created at universities and life science organizations.\nWe encourage you to explore the training platform provided by ELIXIR (Europe’s distributed infrastructure for life-science data). On this platform (TeSS), you can find a registry of training materials as well as webinars, workshops, and in-person courses in bioinformatics, modelling, data management, and life science database usage among other topics."
+    "text": "A priority of the Sandbox is to guide health data science learning using real-world-similar datasets. A major component is addressing how to analyze and leverage person-specific data, such as electronic health records, without invading personal privacy or straying from GDPR guidelines on sensitive data use. We are therefore focused on using either publicly accessible datasets (that are generally well anonymized to enable such release) or we are using/creating synthetic datasets that mimic real-world datasets without replicating real people’s data such that they can be identified. In either case, it is essential for Sandbox users to treat person-specific data respectfully and be aware of the additional responsibility and limitations of working with this type of data as part of their career in health data science.\nWe recommend that users interested in this type of data complete an ethics course on research using health datasets before digging into any analysis. A well regarded course that is also often required for using public databases that contain person-specific data is the Human Subject and Data Research Ethics course designed by the Massachusetts Institute of Technology. The course is hosted at CITI, the Collaborative Institutional Training Initiative. Completing the course is free of charge and provides you with a certificate which you may need to upload to certain databases to gain access. Set up an account at CITI, add an Institutional affiliation with ‘Massachusetts Institute of Technology Affiliates’, and then find and complete the course titled ‘Data or Specimens Only Research’ to obtain a certificate (in pdf form).\n\n\nThe intended scope of the Sandbox is broad, and we will be pulling from many different public access databases in our development of teaching modules. There are classical datasets that serve as benchmark resources for teaching and comparing new methods with old, and also brand new datasets that will support modules on emerging technologies (such as spatial single cell RNA-seq analysis). Databases can be topically broad giant repositories or field-specific, and each may have its own usage rules. We plan to provide our own copies of publically available datasets where allowed to ensure compatibility with the linked module is preserved, but some datasets may need to be downloaded by users themselves under specific access / distribution restrictions.\n\n\n\nThe Sandbox is focused on supporting Danish health data science education and research. Via our collaborators and broader network, we have the opportunity to simulate/synthesize data resembling different databases and registries from the Danish health sector in addition to using traditional data simulation techniques to replicate general datasets. We are exploring methods of creating useful synthetic datasets with local access guidelines/GDPR restrictions in mind, while developing initial datasets using published data from Danish studies and publically available resources."
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-    "text": "News and Events\n\nSandbox data scientists routinely lead or contribute to courses and workshops at host universities in Denmark. Check out upcoming events in the table below!\n\nUpcoming Sandbox Training Events\n\n{{ read_csv(‘../assets/upcomingcourses.csv’) }}\n\n\nNews & Updates\n\n\n07/11/2023 - Proteomics Sandbox Update: Enhanced Security and New Metabolomics Tools (U0niversity of Southern Denmark)\nThe Proteomics Sandbox Application has recently undergone a significant update, enhancing its security features to ensure safer usage for its users. In this latest iteration, the application has expanded its software suite by introducing two new tools: DIA-NN and MZmine, catering to the metabolomics field. Furthermore, the pre-existing software within the application has been refreshed and updated to the latest versions, ensuring that the Proteomics Sandbox Application remains at the cutting-edge of the field. Excitingly, this application will be actively utilized in the course “BMB831: Biostatistics in R II” at the University of Southern Denmark throughout this autumn, showcasing its relevance and applicability in academic settings.\n\n\n25/10/2023 - ‘Research Data Management for NGS Data’ with DeiC (Technical University of Denmark)\nSandbox data scientist Jose Alejandro Romero Herrera ran the first instance of a new module on research data management practices he developed specifically for NGS data. Twelve participants were hosted in conjunction with DeiC at DTU, and were exposed to tools like bash, conda, git, and cookie cutter in their quest to organize their omics data.\n\n\n07/09/2023 - ‘Digging into the Health Data Science Sandbox’ at the Danish Bioinformatics Conference 2023 (Aarhus University)\nThe full team of Sandbox data scientists hosted a 4 hour workshop at the Danish Bioinformatics conference where they gave a taster session of each of our 3 omics apps. We learned that multi-omics analysis were a substantial draw for the crowd at the DBC and are making plans to address this interest in future events.\n\n\n29/08/2023 - 3 days of Sandbox demos at Aarhus University\nSandbox data scientist Samuele Soraggi hosted a three day speed run through Sandbox apps at the Bioinformatics Research Center. The 26 participants joined for genomics, transcriptomics, and/or proteomics app demos depending on their interests. This thorough omics demo had maxed out participant sign-ups and an enthusiastic crew enjoyed the sessions alongside a bit of networking across disciplines. We plan to host more of these type of workshops given the event’s success!\n\n\n19/06/2023 - 3 days of bulk RNA-seq at the University of Copenhagen\nOur teaching team (from the Sandbox, the HeaDS DataLab, and reNEW’s genomics platform) hosted another 3 day workshop on bulk RNA-seq. The 34 participants used the updated version of the UCloud Transcriptomics App which provided the smoothest experience yet for both trainers and trainees. A new goal for the next course run is to add a student project to support independent implementation and exploration of the course content.\n\n\n31/05/2023 - Sandbox App updates on UCloud rolled out\nNew versions of the Genomics Sandbox App, the Transcriptomics Sandbox App, and the Proteomics Sandbox App have all been launched on UCloud this month! Check out the new components in the training modules such as a GWAS module in Genomics and new tools in Transcriptomics and Proteomics. Updates were also informed by the different courses supported during Spring 2023. With these courses wrapping up this month, the associated new training materials have also been included in the new versions of the apps.\n\n\n18/01/2023 - Second bulk RNA-seq course at the University of Copenhagen\nOn 18th of January we taught the second iteration of our bulk RNA-seq course to researchers (from PhD students to professors) at SUND at the University of Copenhagen. We had ~50 workshop participants joining us for three days of lectures and exercises on UCloud. This time, we introduced preprocessing theory (read QC, alignment and quantification) and the use of automated workflows using the nf-core rnaseq pipeline.\nFor those that could not enroll for this session, you can find the updated material here. We have moved the datasets and slides to a zenodo repository\nWe’d like to extend our thanks to our workshop collaborators, data scientists from the SUND DataLab at KU’s Center for Health Data Science as well as the genomics platform at the NNF Center for Stem Cell Medicine (reNEW).\n\n\n10/01/2023 - Sandbox support for Spring 2023 courses\n\nSandbox support for ‘Population Genomics’\nExercises for an MS course on Population Genomics taught by Prof. Kasper Munch at Aarhus University are being implemented on UCloud by Sandbox data scientist Samuele Soraggi. Students will explore the training materials on UCloud during the Spring 2023 semester, after which the materials will be accessible to any UCloud user via the Genomics Sandbox App.\n\n\n‘Fra real-world data til personlig medicin’ with Course Platform & Sandbox support\nThe second round of the course ‘Fra real-world data til personlig medicin’ in KU’s MS in Personlig Medicin begins in January with an introduction to CLL-TIM, a predictive model for chronic lymphocytic leukemia deployed by Prof. Carsten Niemann, an introduction by Sandbox coordinator Jennifer Bartell to the new Course Platform at Computerome built with Sandbox help for hosting courses with HPC resources, and an introduction to building predictive models using TidyModels in R by Prof. Rasmus Broendum. The course will run through April with 10 continuing education students building their own predictive models using a new and improved synthetic CLL dataset developed by Sandbox data scientist Sander Boisen Valentin. Jennifer and Rasmus are also manning the Sandbox Slack workspace to field student questions about the dataset and their model building.\n\n\nSandbox support for ‘Single-cell, Single-Molecule: The Next Level in Cell Biology’\nAn NNF-funded course, ‘Single-cell, Single-Molecule: The Next Level in Cell Biology’ combining experimental and computational approaches to RNA sequencing is starting at Aarhus University. In addition to course-responsible professor Stig Andersen and co-teachers Victoria Birkedal and Thomas Boesen, Sandbox PI Mikkel Schierup will be contributing along with Sandbox data scientist Samuele Soraggi. Samuele is adapting the Transcriptomics App material on UCloud to supply tutorials and exercises for this hefty course as well as serving as a teaching assistant. The course materials will be available to all users of the Transcriptomics Sandbox App on UCloud in the future.\n\n\n\n8/01/2023 - Soft launch of the new Course Platform at Computerome\nSandbox data scientist Jesper Roy Christiansen has been integral to the development of a new ‘Course Platform’ at Computerome, the HPC platform at the Technical University of Denmark. Built as a collaboration between the Sandbox and Computerome, the Course Platform will host its first users, students in ‘Fra real-world data til personlig medicin’, a course of KU’s MS in Personlig Medicin. Sandbox coordinator Jennifer Bartell and Sandbox PI Martin Boegsted have also been involved in testing this new system during course setup. See the above link as well as HPC Access for more details on this platform and how you can also use this new platform to host courses (with or without Sandbox involvement!).\n\n\n30/11/2022 - Sandbox support for ‘Advanced Statistical Learning’\nSandbox data scientist Samuele Soraggi spent two weeks teaching for the Fall 2023 course ‘Advanced Statistical Learning’ taught by Prof. Asger Hobolth at Aarhus University.\n\n\n15/11/2022 - Sandbox support for a workshop in the series ‘Workshops in Applied Bioinformatics’ at SDU\nSandbox data scientist Jacob Fredegaard Hansen created a tutorial on how to use ColabFold for a one day workshop as part of the ‘Workshops in Applied Bioinformatics’ series taught by Sandbox collaborator Veit Schwammle. The material is accessible on the Sandbox website (see Modules, Proteomics) for any UCloud user alongside the UCloud ColabFold App.\n\n\n10/12/2022 - Transcriptomics Sandbox app launched on UCloud!\nWe have deployed our second standalone Sandbox app on UCloud! Please see the Access page for instructions on how to find our Sandbox apps on UCloud - This one is titled ‘Transcriptomics Sandbox’ and module documentation is linked from the UCloud app page as well as here in Modules.\n\n\n06/09/2022 - Genomics Sandbox app launched on UCloud!\nWe have deployed our first standalone Sandbox app on UCloud! Please see the Access page for instructions on how to find our Sandbox apps on UCloud - this first one is titled ‘Genomics Sandbox’ and module documentation is linked from the UCloud app page as well as here in Modules.\n\n\n18/08/2022 - Bulk RNA-seq course at University of Copenhagen\nToday we began teaching our brand new bulk RNA-seq course to researchers (from PhD students to professors) at SUND at the University of Copenhagen. We had 32 workshop participants join us for two days of lectures and exercises on UCloud. We’d like to extend our thanks to our workshop collaborators, data scientists from the SUND DataLab at KU’s Center for Health Data Science as well as the genomics platform at the NNF Center for Stem Cell Medicine (reNEW).\nFor those that could not enroll for this session, you can find the relevant material here.\n\n\n01/06/2022 - Genomics course at Aarhus University\nA month-long course in Genomics taught by Professors Mikkel Schierup and Stig Andersen has started with lead supercomputing support on UCloud by Sandbox data scientist and course instructor Samuele Soraggi. Computational exercises in NGS analysis were deployed in a UCloud project for use by 47 graduate students with primarily molecular biology and clinical backgrounds and no prior supercomputing experience! Post-course update: We received many positive reviews on use of the Genomics Sandbox training materials on UCloud!\n\n\n22/04/2022 - Basics of Personalized Medicine - final wrap-up\nOur first course, Basics of Personalized Medicine, wrapped up this month with student project presentations which described their approaches to analysis of the synthetic Chronic Lymphocytic Leukemia dataset created for the course. Course reviews highlighted the helpfulness of Sandbox staff in troubleshooting R problems and the tremendous amount that students learned about predictive modeling.\n\n\n04/01/2022 - Basics of Personalized Medicine - MS in Personal Medicine program\nThe first course supported by the Sandbox is launching this month - ‘Basics of Personalized Medicine’ - where students in the new Master in Personal Medicine program at University of Copenhagen are introduced to predictive modeling using electronic health records."
+    "text": "Sandbox data scientists routinely lead or contribute to courses and workshops at host universities in Denmark. Check out upcoming events here! All our past events are listed in the table below.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHow I learned to stop worrying and love RDM\n\n\n\nHPC\n\n\nRDM\n\n\nKU\n\n\nworkshop\n\n\n\nConference workshop\n\n\n\nAug 21, 2024\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHPC launch\n\n\n\nHPC\n\n\nRDM\n\n\nKU\n\n\ncourse\n\n\n\nClick to sign-up\n\n\n\nOct 30, 2024\n\n\n\n\n\n\n\n\n\n\n\n\nHPC Pipes\n\n\n\nsnakemake\n\n\nconda\n\n\nKU\n\n\ncourse\n\n\n\nClick to sign-up\n\n\n\nNov 4, 2024\n\n\n\n\n\n\n\n\nNo matching items\n\n\n\n   \n     \n     \n       Order By\n       Default\n         \n          Event title\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\nEvent title\n\n\nDates\n\n\nLocation\n\n\nOrganizers\n\n\n\n\n\n\nIntro to NGS data analysis - Summer School\n\n\n01-05 July 2024\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nWorkshop: Digging into the Health Data Science Sandbox\n\n\n18-19 April 2024\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nCourse support at SDU\n\n\n9 February 2024\n\n\nSDU\n\n\nJacob Fredegaard Hansen\n\n\n\n\nDDSA PhD meetup and D3A conference\n\n\n1 February 2024\n\n\nDDSA\n\n\nJennifer Bartell\n\n\n\n\nA primer for Synthetic health data\n\n\n31 January 2024\n\n\n \n\n\nJennifer Bartell\n\n\n\n\nNNF Collaborative Data Science award news: the SE3D project!\n\n\n12 December 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nUpdates from SDU\n\n\n9 November 2023\n\n\nSDU\n\n\nJacob Fredegaard Hansen\n\n\n\n\nA course on RDS for NGS data\n\n\n7 November 2023\n\n\nDTU\n\n\nJose AR Herrera\n\n\n\n\nFrom Data Chaos to Data Harmony\n\n\n7 November 2023\n\n\nDeiC conference\n\n\nJennifer Bartell\n\n\n\n\n‘Digging into the Health Data Science Sandbox’ workshop\n\n\n7 September 2023\n\n\nAU\n\n\nJennifer Bartell\n\n\n\n\nSandbox workshop in Aarhus\n\n\n29 August 2023\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nWorkshop on bulkRNA-seq data\n\n\n19 June 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nSandbox App updates on UCloud rolled out\n\n\n31 May 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nSecond bulk RNA-seq course at the University of Copenhagen\n\n\n18 January 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nSandbox support for Spring 2023 courses\n\n\n10 January 2023\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nSoft launch of the new Course Platform at Computerome\n\n\n8 January 2023\n\n\nKU\n\n\nJesper R Christiansen\n\n\n\n\nSandbox support for ‘Advanced Statistical Learning’\n\n\n30 November 2022\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nSandbox support within ‘Workshops in Applied Bioinformatics’ at SDU\n\n\n15 November 2022\n\n\nSDU\n\n\nJacob Fredegaard Hansen\n\n\n\n\nTranscriptomics Sandbox app launched on UCloud!\n\n\n15 November 2022\n\n\nKU\n\n\nJose AR Herrera\n\n\n\n\nGenomics Sandbox app launched on UCloud!\n\n\n6 September 2022\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nBulk RNA-seq course at University of Copenhagen\n\n\n18 August 2022\n\n\nKU\n\n\nJennifer Bartell\n\n\n\n\nGenomics course at Aarhus University\n\n\n1 June 2022\n\n\nAU\n\n\nSamuele Soraggi\n\n\n\n\nBasics of Personalized Medicine - final wrap-up\n\n\n22 April 2022\n\n\nAAU\n\n\nJennifer Bartell\n\n\n\n\nBasics of Personalized Medicine - MSc course\n\n\n1 April 2022\n\n\nAAU\n\n\nJennifer Bartell\n\n\n\n\n\nNo matching items"
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+    "text": "A priority of the Sandbox is to guide health data science learning using real-world-similar datasets. A major component is addressing how to analyze and leverage person-specific data, such as electronic health records, without invading personal privacy or straying from GDPR guidelines on sensitive data use. We are therefore focused on using either publicly accessible datasets (that are generally well anonymized to enable such release) or we are using/creating synthetic datasets that mimic real-world datasets without replicating real people’s data such that they can be identified. In either case, it is essential for Sandbox users to treat person-specific data respectfully and be aware of the additional responsibility and limitations of working with this type of data as part of their career in health data science.\nWe recommend that users interested in this type of data complete an ethics course on research using health datasets before digging into any analysis. A well regarded course that is also often required for using public databases that contain person-specific data is the Human Subject and Data Research Ethics course designed by the Massachusetts Institute of Technology. The course is hosted at CITI, the Collaborative Institutional Training Initiative. Completing the course is free of charge and provides you with a certificate which you may need to upload to certain databases to gain access. Set up an account at CITI, add an Institutional affiliation with ‘Massachusetts Institute of Technology Affiliates’, and then find and complete the course titled ‘Data or Specimens Only Research’ to obtain a certificate (in pdf form)."
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+    "text": "A priority of the Sandbox is to guide health data science learning using real-world-similar datasets. A major component is addressing how to analyze and leverage person-specific data, such as electronic health records, without invading personal privacy or straying from GDPR guidelines on sensitive data use. We are therefore focused on using either publicly accessible datasets (that are generally well anonymized to enable such release) or we are using/creating synthetic datasets that mimic real-world datasets without replicating real people’s data such that they can be identified. In either case, it is essential for Sandbox users to treat person-specific data respectfully and be aware of the additional responsibility and limitations of working with this type of data as part of their career in health data science.\nWe recommend that users interested in this type of data complete an ethics course on research using health datasets before digging into any analysis. A well regarded course that is also often required for using public databases that contain person-specific data is the Human Subject and Data Research Ethics course designed by the Massachusetts Institute of Technology. The course is hosted at CITI, the Collaborative Institutional Training Initiative. Completing the course is free of charge and provides you with a certificate which you may need to upload to certain databases to gain access. Set up an account at CITI, add an Institutional affiliation with ‘Massachusetts Institute of Technology Affiliates’, and then find and complete the course titled ‘Data or Specimens Only Research’ to obtain a certificate (in pdf form)."
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-    "text": "Alba is a Sandbox data scientist based at the University of Copenhagen. During her academic background as a PhD and Postdoc she has developed a solid expertise in large-scale genomics and pipelines development on computing clusters."
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+    "text": "Public domain data\nThe intended scope of the Sandbox is broad, and we will be pulling from many different public access databases (especially for training modules on omics analysis). Databases can be topically broad, giant repositories or field-specific, and each may have its own usage rules. We plan to provide our own copies of publically available datasets where allowed to ensure compatibility with the linked module is preserved, but some datasets may need to be downloaded by users themselves under specific access / distribution restrictions. Many omics datasets do not present significant data sensitivity concerns in comparison to real-world data such as electronic health records (EHRs) and clinical trial datasets.\nThere are large public de-identified EHR datasets that serve as benchmark resources for teaching and comparing new methods with old, but these are not numerous and often have restricted usage and sharing terms in addition to being quite dated. Historical approaches to dataset anonymization and de-identification have been substantially challenged in the age of digitalized healthcare and increasing data integration, which means meaningfully large ‘anonymized’ datasets are now rarely released."
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+    "text": "Synthetic data\n\n\n\n\n\n\nVia our collaborators and broader network, the Sandbox has the opportunity to simulate/synthesize data resembling different databases and registries from the Danish health sector. We are exploring methods of creating useful synthetic datasets with national and EU-level data access policies and GDPR restrictions in mind, while developing initial datasets using publicly available data from Danish research studies and other resources.\nUltimately, a new era of synthetic data is rapidly developing. The funded Sandbox proposal focused on generating synthetic data using mechanistic models, agent-based models, or draws from multivariate distributions (such as copulas), which are methods that do not present any significant GDPR-related concerns with sharing the produced datasets as they are derived from population-level characteristics and prior knowledge. However, new deep learning-based methods of data synthesis can theoretically learn complex, nonlinear patterns within a sensitive dataset and generate a synthetic dataset that replicates these patterns. This is a really promising approach for sharing high utility synthetic datasets, but it also elevates risk of accidentally sharing too much about the real dataset and skirting the boundaries of GDPR and ethical data handling. There is an inherent trade-off between privacy preservation and similarity of the synthetic dataset to the original dataset, with method development focused on moving closer to the ideal zone of high privacy AND high similarity. The figure at right is a rough approximation of this relationship versus current families of synthesis methods.\nPlease see Synthetic Data for more information about our approach to this technology."
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-    "text": "Samuele is a Sandbox data scientist based at the university of Aarhus. During his academic activity he has gained experience in population genomics, transcriptomics, single cell multiomics and spans his knowledge across various themes of advanced computational statistics."
+    "text": "Datasets\nHere we provide details of datasets used in our various modules as well as a specific guide on using electronic health record datasets."
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-    "text": "The Health Data Science Sandbox is coordinated by the Center for Health Data Science at the University of Copenhagen (KU). Sandbox data scientists are also placed in collaborating groups at the Technical University of Denmark (DTU), University of Southern Denmark (SDU), Aarhus University (AU), and Aalborg University (AAU).\nTo get in touch with the Sandbox or be connected with Sandbox staff at your university, please email us. To obtain module material for use in your own compute environment, see our GitHub organization page at hds-sandbox.\n\n\n\n\n\n\n\n\n\nMember\nRole\nInstitution\nPI\n\n\n\n\nJennifer Bartell\nProject Manager, Data Scientist\nCenter for Health Data Science, KU\nAnders Krogh\n\n\nAlba Refoyo Martinez\nData Scientist\nCenter for Health Data Science, KU\nAnders Krogh\n\n\nJakob Skelmose\nData Scientist\nDepartment of Clinical Medicine, AAU\nMartin Boegsted\n\n\nSamuele Soraggi\nData Scientist\nBioinformatics Research Centre, AU\nMikkel Schierup\n\n\nJesper Roy Christiansen\nData Scientist\nComputerome, DTU\nPeter Løngreen\n\n\nJacob Fredegaard Hansen\nData Scientist\nDepartment of Biochemistry and Molecular Biology, SDU\nOle Nørregaard Jensen &  Veit Schvämmle\n\n\n\n\nWe appreciate the contributions of previous team members José Alejandro Romero Herrera (KU), Conor O’Hare (KU), Sander Boisen Valentin (AAU) and Peter Husen (SDU).\nFind all the team members and their contacts below. Click on their names for more information about each member of the team:\n\n\n\n\n\n\n\n\n\n\n\n\nAlba Refoyo Martinez\n\n\nData Scientist, Copenhagen University\n\n\n\n\n\n\n\n\n\n\n\nJacob Fredegaard Hansen\n\n\nData Scientist, University of Southern Denmark\n\n\n\n\n\n\n\n\n\n\n\nJakob Skelmose\n\n\nData Scientist, Aalborg University\n\n\n\n\n\n\n\n\n\n\n\nJennifer Bartell\n\n\nData Scientist and Project coordinator, Copenhagen University\n\n\n\n\n\n\n\n\n\n\n\nSamuele Soraggi\n\n\nData Scientist, Aarhus University\n\n\n\n\n\n\n\nNo matching items"
+    "text": "An infrastructure project for health data science training and research in Denmark\nThe National Health Data Science Sandbox project kicked off in 2021 with 5 years of funding via the Data Science Research Infrastructure initiative from the Novo Nordisk Foundation. Health data science experts at five Danish universities are contributing to the Sandbox with coordination from the Center for Health Data Science under lead PI Professor Anders Krogh. Data scientists hosted in the research groups of each PI are building infrastructure and training modules on Computerome and UCloud, the primary academic high performance computing (HPC) platforms in Denmark. If you have any questions or would like to get in touch with one of our data scientists, please contact us here.\n\n\n\n\n\nOur computational ‘sandbox’ allows data scientists to explore datasets, tools and analysis pipelines in the same high performance computing environments where real research projects are conducted. Rather than a single, hefty environment, we’re deploying modularized topical environments tailored for independent use on each HPC platform. We aim to support three key user groups based at Danish universities:\n\ntrainees: use our training modules to learn analysis techniques with some guidance and guardrails - for your data type of interest AND for general good practices for HPC environments\n\nresearchers: prototype your tools and algorithms with an array of good quality datasets that are GDPR compliant and free to access\neducators: develop your next course with computational assignments in the HPC environment your students will use for their research\n\nActivity developing independent training modules and hosting workshops has centered on UCloud, while collaborative construction of a flexible Course Platform has been completed on Computerome for use by the Sandbox and independent educators. Publicly sourced datasets are being used in training modules on UCloud, while generation of synthetic data is an ongoing project at Computerome. Sandbox resources are under active construction, so check out our other pages for the current status on HPC Access, Datasets, and Modules. We run workshops using completed training modules on a regular basis and provide active support for Sandbox-hosted courses through a slack workspace. See our Contact page for more information.\n\n\nPartner with the Sandbox\nThe Sandbox welcomes proposals for new courses, modules, and prototyping projects from researchers and educators. We’d like to partner with lecturers engaged with us in developing needed materials collaboratively - we would love to have input from subject experts or help promote exciting new tools and analysis methods via modules! Please contact us with your ideas at nhds_sandbox@sund.ku.dk.\n\nWe thank the Novo Nordisk Foundation for funding support. If you use the Sandbox for research or reference it in text or presentations, please acknowledge the Health Data Science Sandbox project and its funder the Novo Nordisk Foundation (grant number NNF20OC0063268)."
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+    "text": "Jakob is a Sandbox data scientist based at the university of Aalborg. His work is mainly focused on applications related to synthetic data, both for research and teaching purposes."
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+    "text": "Jacob is a Sandbox data scientist based at the University of Southern Denmark, part of the Protein Research Group in the Department of Biochemistry and Molecular Biology. He specializes in proteomics applications and protein structure prediction tools. Jacob has a background in biochemistry and molecular biology (BSc) and computational biomedicine (MSc). Before delving into health data science at Sandbox, he focused on transcription factor binding specificity for his master’s thesis, analyzing and evaluating it as a biophysical process."
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-    "text": "The Sandbox is collaborating with the two major academic high performance computing platforms in Denmark. Computerome is located at the Technical University of Denmark (and co-owned by the University of Copenhagen) while UCloud is owned by the University of Southern Denmark. These HPC platforms each have their own strengths which we leverage in the Sandbox in different ways."
+    "text": "Jennifer (Jennie) is the Sandbox project manager from the Center for Health Data Science at the University of Copenhagen. Jennie has a broad background in computational systems biology, bioinformatics, and health data modeling. Her PhD and postdoc work also included high throughput phenotyping and long read sequencing of bacterial strain libraries, so she can also speak ‘wet lab’ with experimentalists interested in gaining data science skills. In addition to project management, she’s developing ‘HPC lab’ content and is knowledgable about research data management. She has been deeply involved in the synthetic health data arm of the project, which has turned into a collaborative research project between her, Anders Krogh, Martin Boegsted, and Jan Trzaskowski with a 4 year data science grant from the NNF."
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-    "text": "UCloud\nUCloud is a relatively new HPC platform that can be accessed by students at Danish universities (via a WAYF university login). It has a user friendly graphical user interface that supports straightforward project, user, and resource management. UCloud provides access to many tools via selectable Apps matched with a range of flexible compute resources. Check out UCloud’s extensive user docs here.\nThe Sandbox is deploying training modules in this form such that any UCloud user can easily access Sandbox materials independently. The Sandbox is also hosting workshops and training events on UCloud in conjunction with in-person training. Click below for detailed instructions on accessing Sandbox apps.\n \n\n UCloud Guide for Sandbox apps"
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+    "text": "Welcome to the Health Data Science Sandbox\n\nAccess our training modules\n\n\n\n\n\n\n\n HPC Lab\n\nHPC launch\nHPC pipes\nRDM\n\n\n\n Genomics\n\nNGS data analysis \nPopulation Genomics\nGWAS\n\n\n\n Transcriptomics\n\nBulk RNAseq\nSingle-cell RNAseq\n\n\n\n Proteomics\n\nClinical Proteomics\nColabFold \n\n\n\n Health records\n\nSynthetic data \nPersonalized Medicine \n\n\n\n\n\nWe are a collaborative project with team members spanning five Danish universities\n\nThe Health Data Science Sandbox is a national project coordinated by the Center for Health Data Science at the University of Copenhagen. We’re working with a network of health data science experts to build training resources on academic supercomputers for students and researchers in Denmark. Our Sandbox contains training modules that pair topical datasets with recommended analysis tools, pipelines, and learning materials/tutorials in a portable, containerized format.\n\n\n\n\n\n\nTo get involved as a trainee, researcher, or educator in Denmark:\nTRAINEES: join our next scheduled workshop or a supported university course\nTRAINEES/RESEARCHERS: explore training modules independently on UCloud\nRESEARCHERS: adapt training modules or code repositories to your research\nEDUCATORS: host a training event or course in the Sandbox with our support\n\n \n\n\n\n\n\n\nA note on Sandbox data policy\n\n\n\nThe Sandbox aims to be a resource for learning new analysis approaches and tools for health data science on useful, interesting, and safe-to-share datasets. All person-specific datasets in the Sandbox are non-sensitive and GDPR-safe because they are 1) sourced from public databases, 2) fully anonymous/non-sensitive from a GDPR perspective, and/or 3) synthetic. To learn more, check out Datasets where we explain our data policy in detail and our approach to synthetic data generation.\n\n\nThanks to the Novo Nordisk Foundation for funding the National Health Data Science Project! Please give credit if you use our open-source materials in any form (NNF grant number NNF20OC0063268)."
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-    "text": "Computerome\nComputerome is the home of many sensitive health datasets via collaborations between DTU, KU, Rigshospitalet, and other major health sector players in the Capital Region of Denmark. Computerome has recently launched their secure cloud platform, DELPHI, and in collaboration with the Sandbox has built a Course Platform on the same backbone such that courses and training can be conducted in the same environment as real research would be performed in the secure cloud. The Sandbox is supporting courses in the Course Platform, but it is also available for independent use by educators at Danish universities. Please see their website for more information on independent use and pricing, and contact us if you’d like to collaborate on hosting a course on Computerome. We can help with tool installation, environment testing, and user support (ranging from using the environment to course content if we have Sandbox staff with matching expertise).\nParticipants in courses co-hosted by the Sandbox can check here for access instructions."
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+    "text": "High-Performance Computing (HPC) is crucial for researchers because it provides the computational power necessary to tackle complex and data-intensive problems. HPC systems, with their advanced processing capabilities, allow researchers to perform tasks that would be impractical or impossible with standard computers. UCloud is one such example, designed to be user-friendly with an intuitive graphical interface. It is flexible and extensible to handle multi-scale, multi-disciplinary research challenges, making complex digital technology accessible to all users.\nUser accounts on UCloud are enabled by university login credentials using WAYF (Where Are You From). Access the WAYF login portal here, and then find your affiliated Danish university using the search bar. After login, we suggest setting up Two Factor Authentication by clicking on the icon in the top-right corner of the screen. Once you are an approved user of UCloud, you can access the Sandbox environment via different ‘Sandbox’ apps linked to topical modules that you deploy using your own storage and computing resources - just go to Apps once you have signed into UCloud and search ‘Sandbox’ to find what we have deployed. Each app page has its own Documentation link that will direct you to Sandbox-based usage guidelines which may be customized to the app’s particular tools and scope. Apps will feature various modules that you can select initially, creating a personal copy of the training materials in your workspace for you to edit.\nEach Danish university has its usage relationship with UCloud as governed by their local front office of DeiC - check with your university IT support / DeiC representatives about requesting computational resources. For example, the University of Copenhagen has previously allotted an initial chunk of free UCloud compute hours to staff (from PhD students to professors as well as non-academic staff). If you have further questions about getting compute resources, please contact Sandbox staff.\nExtensive documentation on the general use of UCloud (how to use apps and run jobs, etc.) is available in the UCloud user guide.\n\n\n\n\n\n\nTip\n\n\n\nClick on the images to view them in full size.\n\n\n\n\n\n\nLog onto UCloud at the address http://cloud.sdu.dk using university credentials.\n\n\n\nWhen you are logged in, choose the project from the dashboard (top-right side) from which you would like to utilize compute resources. Every user has their personal workspace (My workspace). You can also provision your own project (check with your local DeiC office if you’re new to UCloud) or you can be invited to someone else’s project. If you’ve previously selected a project, it will be launched by default. If it’s your first time, you’ll be in your workspace. If you’ve joined one of our courses or workshops, your instructor will let you know which to choose.\nFor this example, we select Sandbox RNAseq workshop.\n\n\n\nDashboard: your workspace\n\n\nOn the left side, you can see the structure of the project (content changes when you select a different project):\n\nFiles: all folders/files you have access to. You can navigate through folders, download, upload, or share files with collaborators. You might have varying rights across folders, mostly depending on whether they are yours or have been shared with you\nProject: details\nResources: allocated to your workspace or a project (shared)\napplications: gain access to the apps catalog on ucloud. We refer to apps as the software applications that can be deployed on the cloud. It’s recommended to explore the featured ones. Use the search bar to find the sandbox apps\nRuns: from where you submit your jobs and past runs information\n\n\n\n\n\n\n\nImportant\n\n\n\nDon’t forget to accept the invitation to access new projects. Remember to switch projects to access other files and resources. Test switching among projects and observe how the dashboard changes.\n\n\nAt the bottom left corner, you will find your user ID, which you may need to provide once the course starts or for future collaborations, such as being added to other people’s projects. You can also find it on UCloud docs.\n\n\n\nDashboard: bottom-left menu\n\n\nIn the dashboard, you will also find news and UCloud releases, recent runs, resources allocations, and other notifications between other applications:\n\nResource allocations: indicate your currently allocated resources (e.g., KU employees have access to 1000kr in computing).\nGrant applications: apply for more resources (computing or storage if you run out of them)\n\n\n\n\nThen click on Apps in the left panel to investigate what tools and environments you can use (orange square). The easiest way to find Sandbox resources is to search via the toolbar (red circle). In this example, we’ll select the Genomics Sandbox (which will bring you to the submission screen).\n\n\n\nDashboard: all apps\n\n\n\n\n\n\n\n\nTip\n\n\n\nMark them as favorites so they appear on your dashboard.\n\n\n\n\n\nClick on the app button to get into the settings window. First, we recommend reading the documentation of the app (step 2). Then, you can configure the app as shown below, or be provided with a configuration file made available in a workshop’s project folders (import parameters) which will take care of everything for you.\n\n\n\nDashboard jobs: configuration step\n\n\nIn this example, we configure our session by:\n\nName and version of the app to run\nRead the documentation before using any app\nImport parameters (from previous runs or JSON files tailored for the app)\nJob settings: enter a job name (descriptive of the task), select the time (in hours) we want to use a node for (it can be modified afterwards!) and the machine type (selecting a 4 CPU standard node with 24 GB memory)\nOptional: add folders to access while in this job (e.g.: /home)\nChoose the module in the app you want to run (some apps have several modules that load different notebooks and data)\nClick on the Submit button (and wait!)\n\n\n\n\n\n\n\nImportant\n\n\n\nStep 4 sets up our computing resources for the period we want to work and can be customized as needed. However, only the time can be modified after submitting the job. For some of the Sandbox apps, you might want to select folders (Home and the Notebooks/Data from the module to avoid downloading it every time you start a new job). If you are in doubt, read the documentation specific to the app you are interested in.\nSelect the version of the app (if in doubt, use the latest one). This allows you to run specific versions of software.\n\n\nThere are different types of apps, and therefore, interfaces. Some, like RStudio or Jupyter Notebooks, have their own graphical user interface, whereas others are command-line interfaces. Lastly, you can also deploy a virtual desktop and virtual machine, which allow you to spin up a virtual computer.\n\n\n\nWait to go through the queue. When the session starts, the timer begins to count down. In a couple of minutes, you should be able to open the interface through the button (Open interface) in a new window (refresh the window if needed).\n\n\n\nDashboard jobs: running the app\n\n\nThis page will remain open while you work (or you can return to it via Runs in the left panel). You can end your session early by pressing and holding Stop application (red button), you can see how much time you have left and can add hours to your session as you go (blue buttons in orange square).\n\n\n\nDifferent apps might employ distinct development environments, so your interface experience could vary accordingly. If you’re utilizing an RStudio-based application, like the transcriptomics tool, your interface will launch in a new tab, resembling the image provided below. Simply navigate through the folders to locate and access the R Markdown notebooks.\n\n\n\nRStudio interface: running the app\n\n\nIf you are testing a JupyterLab-based application, such as the genomic app, your interface should look like in the image below. In this case, you will be working from JupyterLab. You can open Jupyter Notebooks (yellow square), R studio (blue square) or a terminal (black square) among others. In this case, the highlighted buttons (under Notebooks) have all the software and packages that you will need pre-installed (this is not the case with Python 3 to the left).\n\n\n\nJupyterLab interface: running the app\n\n\nYou can navigate through the different folders and start running the Python notebooks (orange square).\n\n\n\nJupyterLab interface: opening notebook\n\n\nIf you are an advanced user, you can also create your own Python files and select the Kernel NGS (python) to use the pre-installed software. Learn how to manage (upload and download new data) and share files that you have created/developed with collaborators here.\n\n\n\n\n\n\nTip\n\n\n\nCreate your own directories to save the output of your jobs. You will be able to access them later in your project folders under the resources you are using\nIf you haven’t created any directories, look for the generated files under a folder with the same name as the job name you used.\n\n\nYou are ready to start using Ucloud and the sandbox tools!"
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+    "text": "Log onto UCloud at the address http://cloud.sdu.dk using university credentials.\n\n\n\nWhen you are logged in, choose the project from the dashboard (top-right side) from which you would like to utilize compute resources. Every user has their personal workspace (My workspace). You can also provision your own project (check with your local DeiC office if you’re new to UCloud) or you can be invited to someone else’s project. If you’ve previously selected a project, it will be launched by default. If it’s your first time, you’ll be in your workspace. If you’ve joined one of our courses or workshops, your instructor will let you know which to choose.\nFor this example, we select Sandbox RNAseq workshop.\n\n\n\nDashboard: your workspace\n\n\nOn the left side, you can see the structure of the project (content changes when you select a different project):\n\nFiles: all folders/files you have access to. 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You can also find it on UCloud docs.\n\n\n\nDashboard: bottom-left menu\n\n\nIn the dashboard, you will also find news and UCloud releases, recent runs, resources allocations, and other notifications between other applications:\n\nResource allocations: indicate your currently allocated resources (e.g., KU employees have access to 1000kr in computing).\nGrant applications: apply for more resources (computing or storage if you run out of them)\n\n\n\n\nThen click on Apps in the left panel to investigate what tools and environments you can use (orange square). The easiest way to find Sandbox resources is to search via the toolbar (red circle). In this example, we’ll select the Genomics Sandbox (which will bring you to the submission screen).\n\n\n\nDashboard: all apps\n\n\n\n\n\n\n\n\nTip\n\n\n\nMark them as favorites so they appear on your dashboard.\n\n\n\n\n\nClick on the app button to get into the settings window. First, we recommend reading the documentation of the app (step 2). Then, you can configure the app as shown below, or be provided with a configuration file made available in a workshop’s project folders (import parameters) which will take care of everything for you.\n\n\n\nDashboard jobs: configuration step\n\n\nIn this example, we configure our session by:\n\nName and version of the app to run\nRead the documentation before using any app\nImport parameters (from previous runs or JSON files tailored for the app)\nJob settings: enter a job name (descriptive of the task), select the time (in hours) we want to use a node for (it can be modified afterwards!) and the machine type (selecting a 4 CPU standard node with 24 GB memory)\nOptional: add folders to access while in this job (e.g.: /home)\nChoose the module in the app you want to run (some apps have several modules that load different notebooks and data)\nClick on the Submit button (and wait!)\n\n\n\n\n\n\n\nImportant\n\n\n\nStep 4 sets up our computing resources for the period we want to work and can be customized as needed. However, only the time can be modified after submitting the job. For some of the Sandbox apps, you might want to select folders (Home and the Notebooks/Data from the module to avoid downloading it every time you start a new job). If you are in doubt, read the documentation specific to the app you are interested in.\nSelect the version of the app (if in doubt, use the latest one). This allows you to run specific versions of software.\n\n\nThere are different types of apps, and therefore, interfaces. Some, like RStudio or Jupyter Notebooks, have their own graphical user interface, whereas others are command-line interfaces. Lastly, you can also deploy a virtual desktop and virtual machine, which allow you to spin up a virtual computer.\n\n\n\nWait to go through the queue. When the session starts, the timer begins to count down. In a couple of minutes, you should be able to open the interface through the button (Open interface) in a new window (refresh the window if needed).\n\n\n\nDashboard jobs: running the app\n\n\nThis page will remain open while you work (or you can return to it via Runs in the left panel). You can end your session early by pressing and holding Stop application (red button), you can see how much time you have left and can add hours to your session as you go (blue buttons in orange square).\n\n\n\nDifferent apps might employ distinct development environments, so your interface experience could vary accordingly. If you’re utilizing an RStudio-based application, like the transcriptomics tool, your interface will launch in a new tab, resembling the image provided below. Simply navigate through the folders to locate and access the R Markdown notebooks.\n\n\n\nRStudio interface: running the app\n\n\nIf you are testing a JupyterLab-based application, such as the genomic app, your interface should look like in the image below. In this case, you will be working from JupyterLab. You can open Jupyter Notebooks (yellow square), R studio (blue square) or a terminal (black square) among others. In this case, the highlighted buttons (under Notebooks) have all the software and packages that you will need pre-installed (this is not the case with Python 3 to the left).\n\n\n\nJupyterLab interface: running the app\n\n\nYou can navigate through the different folders and start running the Python notebooks (orange square).\n\n\n\nJupyterLab interface: opening notebook\n\n\nIf you are an advanced user, you can also create your own Python files and select the Kernel NGS (python) to use the pre-installed software. Learn how to manage (upload and download new data) and share files that you have created/developed with collaborators here.\n\n\n\n\n\n\nTip\n\n\n\nCreate your own directories to save the output of your jobs. You will be able to access them later in your project folders under the resources you are using\nIf you haven’t created any directories, look for the generated files under a folder with the same name as the job name you used.\n\n\nYou are ready to start using Ucloud and the sandbox tools!"
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+    "text": "Accessing the Sandbox on Computerome\nWe do not currently support independent use of Sandbox materials on Computerome. Access is supported via courses collaborating with the Sandbox and run on Computerome’s Course Platform. Check here for more info.\nThe below instructions are provided as reference for course participants.\nTo set up a user account on Computerome, you will need to provide administrators with your name, email address, and phone number for two-factor authentication. Once approved as a user, you will receive your username and server address (URL) by email, and you will receive an initial course-platform password by text.\nOn Computerome, the Sandbox environment is deployed as a virtual machine with a Linux desktop as user interface. This environment can be accessed through VMware Horizon using two different methods: (A) a desktop client (which you install on your computer) or (B) a web-based client (for those without install privileges on their computer). Please follow the appropriate instructions (A versus B) depending on your access preference.\n!!! note “Sign-In Instructions” 1. On FIRST login, enter the provided server address (URL) in a browser window to access the environment using your provided credentials. The URL will take you to a VMware Horizon access portal where you can * (A) choose to install the desktop client (left: ‘Install VMware Horizon Client’). You will then open this client for all subsequent logins instead of using the server address, and can login starting from step 2. * (B) access the environment via browser (right: ‘VMware Horizon HTML Access’). You will always use the server address in your browswer to access this entry point if this is your chosen method of access.\n2. Select the cloud icon\n  *  (A) which is linked to the server URL. This option appears when you have successfully installed and opened the VMware Horizon client.\n  *  (B) which is linked to the Sandbox course. This option appears after you have selected VMware Horizon HTML Access.\n\n3. Enter your username and your course-platform password. \n  *  On the first sign-in, this will be the course-platform password texted to you. You will then be prompted to create your own permanent password to replace this password which you will use for all future sign-ins.\n\n4. When prompted, enter the one-time password texted to you from DTU (NOT the same password as the course-platform password).\n  *  (A) If it is your first login / you logged off at last access, press any key when greeted with the blue time status screen. This will allow you to select your own user account in a dialog box.\n\n5. Sign-in using your course-platform password again after choosing the correct language for the environment in the upper right corner of the screen (this is important for the keyboard and typing your password). Danish (the da option) is default, so those with English keyboards will need to switch to English (the en option) at every login.\n\n6. Congratulations, you have entered the Sandbox environment. Relevant links for courses should be present on your desktop.\n!!! warning “Exit Instructions” To exit the environment, you have two options with different outcomes. You can log off and kill all running processes, or you can disconnect and your processes will continue running. “Power off” is disabled for users as this will shut down your virtual machine, local settings and user files may be lost, and the virtual machine will need to be manually restarted for your account.\n1. To exit and kill all running processes, select the power icon in the upper right corner, then select your name and choose \"log off\" in the pop up window.\n2. To exit and preserve running processes,\n    *  (A) hover at the top of the screen for a few seconds until your VMware Client menu is accessible, choose \"Connection\", and select \"Disconnect\".\n    *  (B) close the browser tab where you are accessing the Sandbox environment."
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+    "text": ":material-web-plus: Course Page\n\nThis workshop material includes a tutorial on how to approach RNAseq data, starting from your count matrix. Thus, the workshop only briefly touches upon laboratory protocols, library preparation, and experimental design of RNA sequencing experiments, mainly for the purpose of outlining considerations in the downstream bioinformatic analysis. This workshop is based on the materials developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC), a collection of modified tutorials from the DESeq2 and R language vignettes.\nIntended use: The aim of this repository is to run a comprehensive but introductory workshop on bulk-RNAseq bioinformatic analyses. Each of the modules of this workshop is accompanied by a powerpoint slideshow explaining the steps and the theory behind a typical bioinformatics analysis (ideally with a teacher). Many of the slides are annotated with extra information and/or point to original sources for extra reading material.\n\n\nBy the end of this workshop, you should be able to analyse your own bulk RNAseq count matrix:\n\nNormalize your data.\nExplore your samples with PCAs and heatmaps.\nPerform Differential Expression Analysis.\nAnnotate your results.\n\n!!! agenda “Syllabus” 1. Course Introduction 2. Setup 3. Experimental planning 4. Data Explanation 5. Preprocessing 6. RNAseq counts 7. Exploratory analysis 8. Differential Expression Analysis 9. Functional Analysis 10. Summarized workflow\n!!! info “Workshop prerequisites” - Knowledge of R, Rstudio and Rmarkdown. It is recommended that you have at least followed our workshop R basics - Basic knowledge of RNAseq technology - Basic knowledge of data science and statistics such as PCA, clustering and statistical testing\n\n\n\nCenter for Health Data Science, University of Copenhagen.\nHugo Tavares, Bioinformatics Training Facility, University of Cambridge.\nSilvia Raineri, Center for Stem Cell Medicine (reNew), University of Copenhagen.\nHarvard Chan Bioinformatics Core (HBC), check out their github repo"
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+    "text": "By the end of this workshop, you should be able to analyse your own bulk RNAseq count matrix:\n\nNormalize your data.\nExplore your samples with PCAs and heatmaps.\nPerform Differential Expression Analysis.\nAnnotate your results.\n\n!!! agenda “Syllabus” 1. Course Introduction 2. Setup 3. Experimental planning 4. Data Explanation 5. Preprocessing 6. RNAseq counts 7. Exploratory analysis 8. Differential Expression Analysis 9. Functional Analysis 10. Summarized workflow\n!!! info “Workshop prerequisites” - Knowledge of R, Rstudio and Rmarkdown. It is recommended that you have at least followed our workshop R basics - Basic knowledge of RNAseq technology - Basic knowledge of data science and statistics such as PCA, clustering and statistical testing\n\n\n\nCenter for Health Data Science, University of Copenhagen.\nHugo Tavares, Bioinformatics Training Facility, University of Cambridge.\nSilvia Raineri, Center for Stem Cell Medicine (reNew), University of Copenhagen.\nHarvard Chan Bioinformatics Core (HBC), check out their github repo"
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+    "text": "AlphaFold\n:fontawesome-brands-github: GitHub Repository\nUpdated: January 2022\nStatus: Under expansion\nThis module will contain a basic standalone tutorial on how to run the newly implemented AlphaFold app in the Sandbox (UCloud version).\nIntended use: The aim of this repository is to on-board users for AlphaFold on Computerome/UCloud.\n!!! abstract “Syllabus” 1. Introduction to protein structural analysis 2. Evaluating predicted structures (AlphaFold DB) 3. Using the AlphaFold app to predict new structures (AlphaFold) 4. Replicating an AlphaFold study 5. Future developments possible with AlphaFold\n!!! info “Workshop requirements” Knowledge of Python and Jupyter notebooks\n\nAcknowledgements\n\nCenter for Health Data Science, University of Copenhagen."
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+    "text": "Genomics\nGenomics is the study of genomes, the complete set of an organism’s DNA. Genomics research now encompasses functional and structural studies, epigenomics, and metagenomics, and genomic medicine is under active implementation and extension in the health sector.\nModules linked to genomics topics are currently under construction."
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+    "text": "Sandbox resources have been organized as training modules focused on key topics in health data science. We are constantly adding additional resources and have plans to create additional modules on medical imaging and wearable device data. Feel free to adapt these resources for your own purposes (with credit to the National Health Data Science Sandbox project and other projects they acknowledge in the specific materials).\nYou can access our training modules through:"
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+    "text": "Data Carpentry and management\n\n\n\n\n\n\n\nComputing skills are an important foundation for health data science (and using the above training modules), but formal training is often lacking as biomedical researchers navigate increasingly difficult computational tasks in their projects. These skills range from programming to the use of high-performance computers (HPC) to proper research data management.\n\nRDM for biodata (workshop on how to handle NGS data following simple guidelines to increase the FAIRability of your data)\nHPC launch (workshop in development)\nHPC pipes (workshop in development)\nHeaDS DataLab workshop materials (workshops for programming and good practices developed by the Center for Health Data Science at the University of Copenhagen, which are sometimes co-taught by Sandbox staff! Includes R, python, bash, and git!)"
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-    "text": "Electronic Health Records\nElectronic health records (EHRs) are digital records kept in the public health sector that record the medical histories of individuals, and access is normally highly restricted to preserve patient privacy. This data is sometimes also shared (partly or in full) in secondary patient registries that support research of a specific disease or condition (such as cystic fibrosis). These datasets are extraordinarily valuable in the development of predictive models used in precision medicine.\nModules linked to EHR analysis are currently under development."
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+    "text": "Genomics\n\n\n\n\n\n\n\nGenomics is the study of genomes, the complete set of an organism’s DNA. Genomics research now encompasses functional and structural studies, epigenomics, and metagenomics, and genomic medicine is under active implementation and extension in the health sector.\nUse the Genomics Sandbox App on UCloud to explore the resources below:\n\nIntroduction to Next Generation Sequencing data (last update: June 2023)\nIntroduction to Population Genomics (implementation of a course by Prof. Kasper Munch of Aarhus University) (last update: March 2023)\nIntroduction to GWAS (last update: March 2023)"
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-    "text": ":fontawesome-brands-github: GitHub Repository\nUpdated: January 2021\nStatus: Under expansion\nThe general strategy for the clinical proteomics module is to provide software, computing resources, datsets and storage using UCloud. Written material (instructions etc.), example notebooks and other auxiliary files will be provided in a Github repository.\n\nProteomics Sandbox app will be used for GUI programs\n\nPrimarily for identification / quantification\nFragPipe / MSFragger for database search (and perhaps open search)\nPDV for visualizing spectral matches\nSearchGUI and PeptideShaker also available\n\nJupyterLab app for data analysis after quantification\n\nInit script to activate conda environment and install custom kernel\nNotebooks provided through Github (https://github.com/hds-sandbox/proteomics-course)\n\nDatasets, (installed) software and JSON config files stored in UCloud project folders\n\nStudents currently need to be project members\n\n\nIntended use: Self-guided introduction to basic proteomics\n!!! abstract “Syllabus” 1. Identify and quantify peptides/proteins * “Database search” using MSFragger/FragPipe or MaxQuant * Visualize peptide spectrum matches using e.g. PDV, IDPicker, IPSA, … 2. Quality control analysis 3. Bioinformatics * Reintegrate clinical metadata * JupyterLab / RStudio + e.g. PolySTest / VSClust / … 4. PhosphoProteomics\n!!! info “Workshop requirements” Knowledge of Python and Jupyter Notebooks\n\n\n\nBMB online computational proteomics course\nNordBioNet summer school 2021 (workshops)\nIntroduction to bioinformatics for proteomics - Prof. Harald Barsnes, University of Bergen\nQC workshop and Quantitative Analysis workshop, long 2019 version - Prof. Veit Schwammle, University of Southern Denmark\nSimulation of proteomics data - Dr. Marie Locard-Paulet, University of Copenhagen\nProteogenomics - Dr. Marc Vaudel, University of Bergen\n\n\n\n\nCenter for Health Data Science, University of Copenhagen."
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+    "text": "Transcriptomics\n\n\n\n\n\n\n\nTranscriptomics is the study of transcriptomes, which investigates RNA transcripts within a cell or tissue to determine what genes are being expressed and in what proportion. These RNA transcripts include mRNAs, tRNA, rRNA, and other non-coding RNA present in a cell.\nUse the Transcriptomics Sandbox App on UCloud to explore these resources:\n\nBulk RNAseq (last update: May 2024)\nSingle-Cell RNAseq (last update: May 2023)\nCirrocumulus (a popular tool for visualizing different types of RNA-seq data and downstream analysis)\nRNAseq in RStudio (RStudio session with pre-installed RNAseq analysis packages for exploring with your own uploaded data)"
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-    "text": "BMB online computational proteomics course\nNordBioNet summer school 2021 (workshops)\nIntroduction to bioinformatics for proteomics - Prof. Harald Barsnes, University of Bergen\nQC workshop and Quantitative Analysis workshop, long 2019 version - Prof. Veit Schwammle, University of Southern Denmark\nSimulation of proteomics data - Dr. Marie Locard-Paulet, University of Copenhagen\nProteogenomics - Dr. Marc Vaudel, University of Bergen\n\n\n\n\nCenter for Health Data Science, University of Copenhagen."
+    "objectID": "modules/index.html#proteomics",
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+    "text": "Proteomics\n\n\n\n\n\n\n\nProteomics is the study of proteins that are produced by an organism. Proteomics allows us to analyze protein composition and structure, which have great importance in determining their function.\nUse the Proteomics Sandbox App on UCloud to explore pre-installed tools for proteomics analysis and other resources:\n\nProteomics Sandbox Documentation (last update: May 2023)\nIntroduction to Clinical Proteomics (course under development)\n\nWe also offer a tutorial on UCloud’s ColabFold app, a tool that allows predictions with AlphaFold2 or RoseTTAFold.\n\nColabFold Intro (last update: October 2022)"
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-    "text": "Proteomics\nProteomics is the study of proteins summed across a complete sample (ranging from a single cell to a whole organism). High-throughput measurement is conducted using mass spectrometry techniques and protein arrays, and provides insight into protein expression profiles and interactions."
+    "objectID": "modules/index.html#EHC",
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+    "title": "Training modules",
+    "section": "Electronic Health Records",
+    "text": "Electronic Health Records\n\n\n\n\n\n\n\nElectronic health records (EHRs) are digital records kept in the public health sector that record the medical histories of individuals, and access is normally highly restricted to preserve patient privacy. This data is sometimes also shared (partly or in full) in secondary patient registries that support research on a specific disease or condition (such as breast cancer or cystic fibrosis). These datasets are extraordinarily valuable in the development of predictive models used in precision medicine.\nThe chronic lymphocytic leukemia synthetic dataset listed below is generated solely from public data. It is of low utility, so we don’t recommend its use beyond the course it was designed for (with much explanation for the students on its construction and caveats). Please see Synthetic Data for more information.\n\nChronic Lymphocytic Leukemia synthetic dataset created for use in “Fra realworld data til personlig medicin”, a course from the University of Copenhagen’s MS in Personlig Medicin (last update: January 2023)\nIntro to EHR analysis (workshop under development)"
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+    "title": "\nSandbox Workshop\n",
     "section": "",
-    "text": "Jose Alejandro Romero Herrera :custom-orcid: :simple-github:"
+    "text": "Sandbox Workshop\n!!! info “Upcoming Workshop at AAU”  Intro to the Health Data Science Sandbox at Aalborg University"
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-    "text": "CRediT table\n\n\n\nCRediT role\nInitials\n\n\n\n\nConceptualization\n\n\n\nData curation\n\n\n\nFormal Analysis\n\n\n\nFunding acquisition\n\n\n\nInvestigation\n\n\n\nMethodology\n\n\n\nProject administration\n\n\n\nResources\n\n\n\nSoftware\n\n\n\nSupervision\n\n\n\nValidation\n\n\n\nVisualization\n\n\n\nWriting - original draft\n\n\n\nWriting - review & editing"
+    "objectID": "workshop/workshopAAU_2023.html#the-sandbox-concept",
+    "href": "workshop/workshopAAU_2023.html#the-sandbox-concept",
+    "title": "\nSandbox Workshop\n",
+    "section": "The Sandbox concept",
+    "text": "The Sandbox concept\nThe Health Data Science Sandbox aims to be a training resource for bioinformaticians, data scientists, and those generally curious about how to investigate large biomedical datasets. We are an active and developing project seeking interested users (both trainees and educators). All of our open-source materials are available on our Github page and much more information is available on the rest of the website you are currently visiting! We work with both UCloud and Computerome (major Danish academic supercomputers) - see our HPC Access page for more info on each set up."
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+    "title": "\nSandbox Workshop\n",
     "section": "Access Sandbox resources",
-    "text": "Access Sandbox resources\nOur first choice is to provide all the training materials, tutorials, and tools as interactive apps on UCloud, the supercomputer located at the University of Southern Denmark. Anyone using these resources needs the following:\n\na Danish university ID so you can sign on to UCloud via WAYF1.\n\n \n\n for UCloud Access click here \n\n \n\nbasic ability to navigate in Linux/RStudio/Jupyter. You don’t need to be an expert, but it is beyond our ambitions (and course material) to teach you how to code from zero and how to run analyses simultaneously. We recommend a basic R or Python course before diving in.\nFor workshop participants: Use our invite link to the correct UCloud workspace that will be shared on the day of the workshop. This way, we can provide you with compute resources for the active sessions of the workshop2 Click the link below after your first uCloud access and accept the invite that shows.\n\n \n\n Invite link to uCloud workspace \n\n   \n\n\n\n\n\n\nNote\n\n\n\nOur apps can run on other clusters, simply by pulling a so-called docker container. You only need to have either docker or singularity installed on the cluster. GenomeDK supports singularity and thus can run our learning material as well. Ask us if you want to help the apps out of uCloud. Instructions will soon be available within our HPC access instructions."
+    "text": "Access Sandbox resources\nWe currently provide training materials and resources as topical apps on UCloud, the supercomputer located at the University of Southern Denmark. To use these resources, you’ll need the following:\n\nLog onto UCloud at the address http://cloud.sdu.dk using your university credentials.\nthe ability to navigate in linux / RStudio / Jupyter. You don’t need to be an expert, but it is beyond our ambitions (and course material) to teach you how to code and how to run analyses simultaneously. We recommend a basic R or Python course before diving in.\n\nNote:\n\nTo use Sandbox materials outside of the workshop, you can request a project by clicking on apply for resources in your uCloud dashboard.\nIf you are a BSc or MSc student, you need a supervisor to apply on your behalf, or you can try to apply yourself mentioning the supervisor approval in the application.\nRemember, however, that you have 1000Kr of computing credit, and around 50GB of free storage to work on uCLoud."
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-    "title": "Sandbox Workshop",
-    "section": "Our OMICS apps",
-    "text": "Our OMICS apps\nThe agenda starts with an introduction to High Performance Computing (HPC) and uCloud. You will try two apps during the workshop, but we are developing others, and have deployed three apps already.\n \n\n\n\nProteomics Sandbox: Our sandbox modern with a suite of proteomics analysis tools, used for example in clinical proteomics. This app is not alone, since our data scientist Jacob has also made the app ColabFold on UCloud, with methods for protein structure prediction.\n\n\n \n\n\n\nTranscriptomics Sandbox : Our sandbox for bulk or single-cell RNA sequencing analysis and visualization - amongst others two regular workshops and provides stand-alone visualization tools. In the next update, we will introduce advanced tutorials for more complex single-cell RNA sequencing analysis from some of our supported courses.\n\n\n \n\n\n\nGenomics Sandbox: Our sandbox NGS data analysis and applications range from genome assembly to variant calling to metagenomics. We have currently a semester-long population genomics course and an NGS course with many applications (alignment, VCF analysis, bulk-RNA data, single-cell RNA sequencing)"
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+    "title": "\nSandbox Workshop\n",
+    "section": "Try out our transcriptomics module",
+    "text": "Try out our transcriptomics module\nSo our Sandbox data scientists have finished their intro at the workshop? Great, now the brave ones in the audience can try out one of our apps in a live session. Today we are demoing:\n ### Transcriptomics If you’re interested in bulk or single cell RNA sequencing analysis and visualization, join Sandbox Data Scientist Samuele Soraggi from Aarhus University in testing out our Transcriptomics Sandbox app.\nFollow these instructions to try our app:\n\nClick on the button below to join the project for today: &lt;!DOCTYPE html&gt;\n\n\n\n\n\n&lt;p&gt;Green Button&lt;/p&gt;\n\n\n\n\n\nGo to Link\n\n\nYou should see a message on your browser where you have to accept the invitation to the project. This will add you to a project on uCloud, where we have data and extra computing credit for the course.\nBe sure you have joined the project. Check if you have the project OMICS workshop from the project menu (red circle). Afterwards, click on the App menu (green circle) \n\nFind the app Transcriptomics Sandbox (red circle), which is under the title Featured.\n\n\n\n\nClick on it. You will get into the settings window. Choose any Job Name (Nr 1 in the figure below), how many hours you want to use for the job (Nr 2; choose at least 3 hours, you can increase this later), and how many CPUs (Nr 3, choose at least 4 CPUs). Choose the course RNAseq in RStudio from the drop-down menu (Nr 4). Finally, click on the blue button Add Folder.\n\n\n\nNow, click on the browsing bar that appears (red circle).\n\n\n\nIn the appearing window, you should see already a folder called Intro_to_scRNAseq_R. Click on Use at its right (red circle)\n\n\n\nAfterwards, you should have something like this in the settings page:\n\n\n\nNow, click on Submit to start the app (the button is on the right side of the settings page)\nYou will now enter a waiting queue. When the session starts, the timer begins to count down (red circle), and you should be able to open the interface through the button (green circle). Note the buttons to add time to your session (blue circle) and the button to stop the session when you are done (pink circle)\n\n\n\nOpen the interface by clicking on the button (green circle of figure above). Sometimes you are warned of a missing connection: simply refresh the page. You will enter Rstudio, well-known interface to code in R.\nRun the following command to download the tutorial: download.file(\"https://raw.githubusercontent.com/hds-sandbox/ELIXIR-workshop/main/Notebooks/scRNAseq_Tutorial_R.Rmd\", \"tutorial_scrna.Rmd\")\nOpen the file tutorial_scrnaR.Rmd that should now appear in the file browser of Rstudio. Click now on visual (on the tool bar) if you need to see the tutorial in a more readable format.\nThe executable code is inside chunks (called cells) to be executed in order from the first to the last using the little green arrow appearing on the right side of each code cell.\nRead carefully through the tutorial and execute the code cells. You will see the outputs appearing as you proceed."
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     "section": "Discussion and feedback",
-    "text": "Discussion and feedback\nWe hope you enjoyed the live demo. If you have broader questions, suggestions, or concerns, now is the time to raise them! If you are totally toast for the day, remember that you can check out longer versions of our tutorials as well as other topics and tools in each of the Sandbox modules or join us for a multi-day in-person course (follow our news here).\nAs data scientists, we also would be really happy for some quantifiable info and feedback - we want to build things that the Danish health data science community is excited to use. Please answer these 5 questions for us before you head out for the day 3.\n \n\n\n\n\n\n\n\nNice meeting you and we hope to see you again!"
+    "text": "Discussion and feedback\nWe hope you enjoyed the live demo. If you have broader questions, suggestions, or concerns, now is the time to raise them! If you are totally toast for the day, remember that you can check out longer versions of our tutorials as well as other topics and tools in each of the Sandbox modules or join us for a multi-day in person course.\nAs data scientists, we also would be really happy for some quantifiable info and feedback - we want to build things that the Danish health data science community is excited to use. Please answer these 5 questions for us before you head out for the day (link activated on day of the workshop).\n\nNice meeting you and we hope to see you again!"
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-    "text": "Footnotes\n\n\nOther institutions (e.g. hospitals, libraries, …) can log on through WAYF. See all institutions here↩︎\nTo use Sandbox materials outside of the workshop: remember that each new user has hundreds of hours of free computing credit and around 50GB of free storage, which can be used to run any uCloud software. If you run out of credit (which takes a long time) you’ll need to check with the local DeiC office at your university about how to request compute hours on UCloud. Contact us at the Sandbox if you need help or want more information.↩︎\nlink activated on day one of the workshop.↩︎"
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+    "title": "\nWelcome to the homepage for our in-person RDM workshop. Thank you for joining us!\n",
+    "section": "Metadata links",
+    "text": "Metadata links\n\n1000 Genomes Project\nHomo sapiens, GRCh38\nIPD-IMGT/HLA database\nPandas package\nDanish registers  \n\nThe Health Data Science Sandbox aims to be a training resource for bioinformaticians, data scientists, and those generally curious about how to investigate large biomedical datasets. We are an active and developing project seeking interested users (both trainees and educators). Our open-source materials are available on our Github page and can be used on a computing cluster! We work with both UCloud, GenomeDK and Computerome, the major Danish academic supercomputers. See our HPC Access page for more info on each setup.\n\n\n\n\n\n\n\nNice meeting you and we hope to see you again!"
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+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../modules/index.html"> 
+<span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link active" href="../workshop/workshop_conf.html" aria-current="page"> 
+<span class="menu-text">RDM Workshop</span></a>
+  </li>  
+  <li class="nav-item dropdown ">
+    <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">
+ <span class="menu-text">Datasets</span>
+    </a>
+    <ul class="dropdown-menu" aria-labelledby="nav-menu-datasets">    
+        <li>
+    <a class="dropdown-item" href="../datasets/datapolicy.html">
+ <span class="dropdown-text">Data policy</span></a>
+  </li>  
+        <li>
+    <a class="dropdown-item" href="../datasets/synthdata.html">
+ <span class="dropdown-text">Synthetic data</span></a>
+  </li>  
+    </ul>
+  </li>
+  <li class="nav-item">
+    <a class="nav-link" href="../about/about.html"> 
+<span class="menu-text">About</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../contact/contact.html"> 
+<span class="menu-text">Contact</span></a>
+  </li>  
+</ul>
+            <ul class="navbar-nav navbar-nav-scroll ms-auto">
+  <li class="nav-item compact">
+    <a class="nav-link" href="https://github.com/hds-sandbox"> <i class="bi bi-github" role="img" aria-label="GitHub">
+</i> 
+<span class="menu-text"></span></a>
+  </li>  
+  <li class="nav-item compact">
+    <a class="nav-link" href="https://www.linkedin.com/company/ucph-heads"> <i class="bi bi-linkedin" role="img" aria-label="LinkedIn">
+</i> 
+<span class="menu-text"></span></a>
+  </li>  
+  <li class="nav-item compact">
+    <a class="nav-link" href="https://twitter.com/ucph_heads"> <i class="bi bi-twitter" role="img" aria-label="Twitter">
+</i> 
+<span class="menu-text"></span></a>
+  </li>  
+</ul>
+          </div> <!-- /navcollapse -->
+            <div class="quarto-navbar-tools">
+  <a href="" class="quarto-color-scheme-toggle quarto-navigation-tool  px-1" onclick="window.quartoToggleColorScheme(); return false;" title="Toggle dark mode"><i class="bi"></i></a>
+</div>
+      </div> <!-- /container-fluid -->
+    </nav>
+</header>
+<!-- content -->
+<div id="quarto-content" class="quarto-container page-columns page-rows-contents page-layout-full page-navbar">
+<!-- sidebar -->
+<!-- margin-sidebar -->
+    
+<!-- main -->
+<main class="content column-page" id="quarto-document-content">
+
+
+
+
+<div class="callout callout-style-simple callout-note no-icon">
+<div class="callout-body d-flex">
+<div class="callout-icon-container">
+<i class="callout-icon no-icon"></i>
+</div>
+<div class="callout-body-container">
+<h1 align="center">
+Welcome to the homepage for our in-person RDM workshop. Thank you for joining us!
+</h1>
+<h3 align="center" style="margin:0px" class="anchored">
+21st August 2024
+</h3>
+</div>
+</div>
+</div>
+<section id="metadata-links" class="level2">
+<h2 class="anchored" data-anchor-id="metadata-links">Metadata links</h2>
+<ul>
+<li><a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/">1000 Genomes Project</a></li>
+<li><a href="https://ftp.ensembl.org/pub/release-111/fasta/homo_sapiens/dna/README">Homo sapiens, GRCh38</a></li>
+<li><a href="https://github.com/ANHIG/IMGTHLA/blob/Latest/README.md">IPD-IMGT/HLA database</a></li>
+<li><a href="https://github.com/pandas-dev/pandas">Pandas package</a></li>
+<li><a href="https://www.dst.dk/extranet/forskningvariabellister/Oversigt%20over%20registre.html">Danish registers</a> &nbsp;</li>
+</ul>
+<p>The Health Data Science Sandbox aims to be a <strong>training resource for bioinformaticians, data scientists, and those generally curious about how to investigate large biomedical datasets</strong>. We are an active and developing project <strong>seeking interested users</strong> (both trainees and educators). Our open-source materials are available on our <a href="https://github.com/hds-sandbox">Github page</a> and can be used on a computing cluster! We work with both <a href="https://cloud/sdu.dk">UCloud</a>, <a href="https://genome.au.dk">GenomeDK</a> and <a href="https://www.computerome.dk/">Computerome</a>, the major Danish academic supercomputers. See our <a href="../access/index.html">HPC Access page</a> for more info on each setup.</p>
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+<i class="callout-icon no-icon"></i>
+</div>
+<div class="callout-body-container">
+<h4 align="center" class="anchored">
+Nice meeting you and we hope to see you again!
+</h4>
+</div>
+</div>
+</div>
+
+
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+          } else {
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+      const cites = parentEl.dataset.cites;
+      if (cites) {
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+          el,
+          cites: cites.split(' ')
+        };
+      } else {
+        return findCites(el.parentElement)
+      }
+    } else {
+      return undefined;
+    }
+  };
+  var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
+  for (var i=0; i<bibliorefs.length; i++) {
+    const ref = bibliorefs[i];
+    const citeInfo = findCites(ref);
+    if (citeInfo) {
+      tippyHover(citeInfo.el, function() {
+        var popup = window.document.createElement('div');
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+          citeDiv.classList.add('csl-entry');
+          var biblioDiv = window.document.getElementById('ref-' + cite);
+          if (biblioDiv) {
+            citeDiv.innerHTML = biblioDiv.innerHTML;
+          }
+          popup.appendChild(citeDiv);
+        });
+        return popup.innerHTML;
+      });
+    }
+  }
+});
+</script>
+</div> <!-- /content -->
+
+
+
+
+</body></html>
\ No newline at end of file
diff --git a/workshop/workshop_june24.html b/workshop/workshop_june24.html
index 23d19be..4f18c6b 100644
--- a/workshop/workshop_june24.html
+++ b/workshop/workshop_june24.html
@@ -107,6 +107,10 @@
   <li class="nav-item">
     <a class="nav-link" href="../modules/index.html"> 
 <span class="menu-text">Modules</span></a>
+  </li>  
+  <li class="nav-item">
+    <a class="nav-link" href="../workshop/workshop_conf.html"> 
+<span class="menu-text">RDM Workshop</span></a>
   </li>  
   <li class="nav-item dropdown ">
     <a class="nav-link dropdown-toggle" href="#" id="nav-menu-datasets" role="link" data-bs-toggle="dropdown" aria-expanded="false">