diff --git a/.nojekyll b/.nojekyll
index 63a86de..ead2af9 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-c88b014a
\ No newline at end of file
+cdef3cec
\ No newline at end of file
diff --git a/index.html b/index.html
index b08f56b..921e2ea 100644
--- a/index.html
+++ b/index.html
@@ -9,7 +9,7 @@
 <meta name="author" content="Julien Brun">
 <meta name="author" content="Renata Curty">
 <meta name="author" content="Greg Janée">
-<meta name="dcterms.date" content="2024-06-27">
+<meta name="dcterms.date" content="2024-06-28">
 
 <title>Designing a Reproducible and Collaborative Lab (RCL)</title>
 <style>
@@ -412,7 +412,7 @@ <h1 class="title">Designing a Reproducible and Collaborative Lab (RCL)</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">June 27, 2024</p>
+      <p class="date">June 28, 2024</p>
     </div>
   </div>
   
@@ -476,13 +476,13 @@ <h3 class="anchored" data-anchor-id="want-to-know-more">Want to know more?</h3>
 <div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" id="quarto-citation"><h2 class="anchored quarto-appendix-heading">Citation</h2><div><div class="quarto-appendix-secondary-label">BibTeX citation:</div><pre class="sourceCode code-with-copy quarto-appendix-bibtex"><code class="sourceCode bibtex">@online{brun2024,
   author = {Brun, Julien and Curty, Renata and Janée, Greg},
   title = {Designing a {Reproducible} and {Collaborative} {Lab} {(RCL)}},
-  date = {2024-06-27},
+  date = {2024-06-28},
   url = {https://ucsb-library-research-data-services.github.io/reproducible-lab/},
   langid = {en}
 }
 </code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre><div class="quarto-appendix-secondary-label">For attribution, please cite this work as:</div><div id="ref-brun2024" class="csl-entry quarto-appendix-citeas" role="listitem">
 Brun, Julien, Renata Curty, and Greg Janée. 2024. <span>“Designing a
-Reproducible and Collaborative Lab (RCL).”</span> June 27, 2024. <a href="https://ucsb-library-research-data-services.github.io/reproducible-lab/">https://ucsb-library-research-data-services.github.io/reproducible-lab/</a>.
+Reproducible and Collaborative Lab (RCL).”</span> June 28, 2024. <a href="https://ucsb-library-research-data-services.github.io/reproducible-lab/">https://ucsb-library-research-data-services.github.io/reproducible-lab/</a>.
 </div></div></section></div></main> <!-- /main -->
 <div>
   <p></p><hr><p></p>
diff --git a/preserve_computing-env.html b/preserve_computing-env.html
index 31e7a70..a3c7cbc 100644
--- a/preserve_computing-env.html
+++ b/preserve_computing-env.html
@@ -406,7 +406,11 @@
     <h2 id="toc-title">On this page</h2>
    
   <ul>
-  <li><a href="#virtual-environments" id="toc-virtual-environments" class="nav-link active" data-scroll-target="#virtual-environments">Virtual environments</a></li>
+  <li><a href="#virtual-environments" id="toc-virtual-environments" class="nav-link active" data-scroll-target="#virtual-environments">Virtual environments</a>
+  <ul class="collapse">
+  <li><a href="#r" id="toc-r" class="nav-link" data-scroll-target="#r">R</a></li>
+  <li><a href="#python" id="toc-python" class="nav-link" data-scroll-target="#python">Python</a></li>
+  </ul></li>
   </ul>
 </nav>
     </div>
@@ -437,12 +441,12 @@ <h1 class="title">Preserve your computing environment</h1>
 <p><code>sessionInfo()</code> or <code>devtools::session_info()</code> are great ways to capture this information. You should save it into a session_info.txt file to include in your GitHub repository</p>
 <p>In Python:</p>
 <p><code>pip freeze &gt; requirements.txt</code> will capture all the python modules installed in your current environment</p>
-<section id="virtual-environments" class="level3">
-<h3 class="anchored" data-anchor-id="virtual-environments">Virtual environments</h3>
+<section id="virtual-environments" class="level2">
+<h2 class="anchored" data-anchor-id="virtual-environments">Virtual environments</h2>
 <p>You can even go a step further and help others to recreate the same computing environment that you used independently of what versions you have installed on your machine.</p>
 <p><a href="https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExemkwd3RnMzA4a3FocHZ2bWJscHgxNG9yaW4yeGZ6YzZpaXgzNHJhciZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/x2woMnCz4W0Vy/giphy.gif">Level up!</a></p>
-<section id="r" class="level4">
-<h4 class="anchored" data-anchor-id="r">R</h4>
+<section id="r" class="level3">
+<h3 class="anchored" data-anchor-id="r">R</h3>
 <p>Here is a short introduction to <code>renv</code>, an R package that creates virtual environment to encapsulate your R work. <img src="../../img/renv.png" class="img-fluid" alt="source: https://rstudio.github.io/renv/articles/renv.html"></p>
 <p>We are going to add renv to our shorebird data cleaning project. Make sure you have the renv package installed:</p>
 <div class="sourceCode" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"renv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
@@ -468,8 +472,8 @@ <h4 class="anchored" data-anchor-id="r">R</h4>
 <p>Use <code>.libPaths()</code> to confirm where package installations are located!</p>
 <p>Want to know more? here is a good resource to get started: <a href="https://rstudio.github.io/renv/articles/renv.html" class="uri">https://rstudio.github.io/renv/articles/renv.html</a></p>
 </section>
-<section id="python" class="level4">
-<h4 class="anchored" data-anchor-id="python">Python</h4>
+<section id="python" class="level3">
+<h3 class="anchored" data-anchor-id="python">Python</h3>
 <p><a href="https://docs.python-guide.org/dev/virtualenvs/#lower-level-virtualenv"><code>virtualenv</code></a> is a tool to create Python virtual environments. In a nutsheel here are the steps to follow:</p>
 <p>Create a new folder, your project folder. I will use EDS213. Make sure you set the path to this folder.</p>
 <ul>
@@ -529,7 +533,6 @@ <h4 class="anchored" data-anchor-id="python">Python</h4>
 <li><p>The environment should be something you should throw away and be able to rebuild easily.</p></li>
 <li><p>Make sure to deactivate when done using it.</p></li>
 </ul>
-</section>
 <section id="if-you-are-using-conda" class="level4">
 <h4 class="anchored" data-anchor-id="if-you-are-using-conda">If you are using Conda</h4>
 <p><strong>Checking what is in the system:</strong></p>
@@ -585,6 +588,7 @@ <h4 class="anchored" data-anchor-id="if-you-are-using-conda">If you are using Co
 <p>More info: <a href="https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html"><u>https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html</u></a></p>
 
 
+</section>
 </section>
 </section>
 
diff --git a/search.json b/search.json
index bf7c4a2..1a65a41 100644
--- a/search.json
+++ b/search.json
@@ -247,7 +247,7 @@
     "href": "index.html",
     "title": "Designing a Reproducible and Collaborative Lab (RCL)",
     "section": "",
-    "text": "The Designing a Reproducible and Collaborative Research Lab program, currently piloted by the Research & Data Services (RDS) department at the UCSB Library, combines group workshops, own-pace learning, and personalized coaching sessions for Faculty and their Team son the following topics.\n\nTopics\n\nCoding as a lab\nManaging your lab data\nAutomating your analytical workflows\nDocumenting & preserving things to make them reusable\n\n\n\nFormat\n\nScheduled at your convenience\n4 workshops (2-3h) as a primer on a specific topic\nPersonalized coaching sessions focused on lab-specific needs\n\n\n\nOutcomes\n\nMore reproducible management of your lab’s scientific products (data, code etc.)\nBetter analytical strategies for collaboration safety nets for your research lab content\nMore efficient reuse of your work\nStreamlined on- and off -boarding of collaborators\n\n\n\nLearning Opportunities\n\nLeverage GitHub to manage and collaborate on your Lab’s code\nDevelop strategies to centralize and manage your Lab’s data\nSet up templates and guidelines on how to document your Lab research and improve its reusability\nEstablish Lab operating procedures and documentation standards\nProtect your Lab’s scientific products against loss through backup and computing resources strategies\n\n\n\nWant to know more?\nIf you are interested in this program, please see the about page to connect with us.\n\n\n\n\n\n\n\n\n\nCitationBibTeX citation:@online{brun2024,\n  author = {Brun, Julien and Curty, Renata and Janée, Greg},\n  title = {Designing a {Reproducible} and {Collaborative} {Lab} {(RCL)}},\n  date = {2024-06-27},\n  url = {https://ucsb-library-research-data-services.github.io/reproducible-lab/},\n  langid = {en}\n}\nFor attribution, please cite this work as:\nBrun, Julien, Renata Curty, and Greg Janée. 2024. “Designing a\nReproducible and Collaborative Lab (RCL).” June 27, 2024. https://ucsb-library-research-data-services.github.io/reproducible-lab/.",
+    "text": "The Designing a Reproducible and Collaborative Research Lab program, currently piloted by the Research & Data Services (RDS) department at the UCSB Library, combines group workshops, own-pace learning, and personalized coaching sessions for Faculty and their Team son the following topics.\n\nTopics\n\nCoding as a lab\nManaging your lab data\nAutomating your analytical workflows\nDocumenting & preserving things to make them reusable\n\n\n\nFormat\n\nScheduled at your convenience\n4 workshops (2-3h) as a primer on a specific topic\nPersonalized coaching sessions focused on lab-specific needs\n\n\n\nOutcomes\n\nMore reproducible management of your lab’s scientific products (data, code etc.)\nBetter analytical strategies for collaboration safety nets for your research lab content\nMore efficient reuse of your work\nStreamlined on- and off -boarding of collaborators\n\n\n\nLearning Opportunities\n\nLeverage GitHub to manage and collaborate on your Lab’s code\nDevelop strategies to centralize and manage your Lab’s data\nSet up templates and guidelines on how to document your Lab research and improve its reusability\nEstablish Lab operating procedures and documentation standards\nProtect your Lab’s scientific products against loss through backup and computing resources strategies\n\n\n\nWant to know more?\nIf you are interested in this program, please see the about page to connect with us.\n\n\n\n\n\n\n\n\n\nCitationBibTeX citation:@online{brun2024,\n  author = {Brun, Julien and Curty, Renata and Janée, Greg},\n  title = {Designing a {Reproducible} and {Collaborative} {Lab} {(RCL)}},\n  date = {2024-06-28},\n  url = {https://ucsb-library-research-data-services.github.io/reproducible-lab/},\n  langid = {en}\n}\nFor attribution, please cite this work as:\nBrun, Julien, Renata Curty, and Greg Janée. 2024. “Designing a\nReproducible and Collaborative Lab (RCL).” June 28, 2024. https://ucsb-library-research-data-services.github.io/reproducible-lab/.",
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-    "text": "Your analysis was done with specific versions of the program used (e.g. R 4.4.3) but also of all the packages involved, as well as the specifications of the Operating System (OS) that was used. The good use is that there are tools to let you systematically capture this information.\nIn R:\nsessionInfo() or devtools::session_info() are great ways to capture this information. You should save it into a session_info.txt file to include in your GitHub repository\nIn Python:\npip freeze &gt; requirements.txt will capture all the python modules installed in your current environment\n\nVirtual environments\nYou can even go a step further and help others to recreate the same computing environment that you used independently of what versions you have installed on your machine.\nLevel up!\n\nR\nHere is a short introduction to renv, an R package that creates virtual environment to encapsulate your R work. \nWe are going to add renv to our shorebird data cleaning project. Make sure you have the renv package installed:\ninstall.packages(\"renv\")\nYou may also choose Tools&gt;Project Options&gt;Environments and check “use renv for this project”\nOr at the R console:\nrenv::init( )\nLet’s check the new files that we have…\n\nLook at the .gitignore\nLook at the renv.lock file\n\nLet’s say we reworked our script and suppose we would need the naniar R package to deal with the NAs. Let’s update or virtual environment:\nrenv::install(\"naniar\")\nAdd some R code using this package, for example\nlibrary(naniar)\n\nsnowsurvey_csv %&gt;% \n  miss_var_summary()\nAlright, now that the installation is completed and we added this package to our code, we can save, and take a snapshot:\nrenv::snapshot()\nThis action will update the lock file, and we will see naniar and all its dependencies included.\nLet’s check. If updated, you should be good to go. If your attempts to update packages introduced new problems, you may run renv::restore() to revert to the previous state as encoded in the lockfile.\nUse .libPaths() to confirm where package installations are located!\nWant to know more? here is a good resource to get started: https://rstudio.github.io/renv/articles/renv.html\n\n\nPython\nvirtualenv is a tool to create Python virtual environments. In a nutsheel here are the steps to follow:\nCreate a new folder, your project folder. I will use EDS213. Make sure you set the path to this folder.\n\nNote: The environment will be created in the current version of Python that you are running (in Conda we can specify the version we want).\n\nTo create the environment: (second venv is the name of the environment)\npython3 -m venv venv213\n\nThe -m flag makes sure you are creating a pip that is tied to the active Python executable\n\nTime to activate it:\nsource venv213/bin/activate\nYou can tell it is activated because it shows (venv213) in the prompt.\nLet’s check which packages are there with a new pip list\nNothing, right? Only setup tools, and pip). Nothing to worry about, it should be this way! Let’s proceed.\nInstall libraries:\nWe will be installing two packages for this exercise.\nFirst:\npip install numpy\nAnd then:\npip install pandas\nThis should take a little longer!\nAnother pip list\nAlright, the packages and dependencies installed are right there!\nExport and allow future replication of the environment:\nLet’s save the packages and dependencies we have after the installs.\npip freeze\nThat should be stored in a `requirements.txt file\nSo let’s get it redirected to the required file:\npip freeze &gt; requirements.txt\nQuestion: This file won’t leave inside the venv folder, but rather in the project root folder any idea why?\nWell, you only need that file to reproduce the environment. And the venv should be should throw away and be able to rebuild easily! So, do not include any project file in that folder and treat that as disposable after the pip freeze\nTo double-check if all is good, we can run the following command:\ncat requirements.txt\nThis file should be included in your repository to let others reinstall your packages and dependencies as needed.\nDeactivate\nIf you are done with that, you should deactivate that environment by typing:\ndeactivate\nThen, you will see you no longer have the environment we created in our prompt.\nIf you are willing to delete the environment altogether, you should delete the directory for the virtual environment\nRemove folder:\nrm -rf venv213/\nReusing the Requirements\nCreate a new project folder to reuse the requirements\nmkdir my-project\nCreate a virtual env for it\npython3 -m venv my-project/venv\nActivate it\nsource my-project/venv/bin/activate\nInstall required packages\npip install -r requirements.txt\n\nAttention! Never include project files in the venv folder.\nDo not commit your venv file to the environment itself to source control (git ignore)\nYou may install more packages and update the requirements.txt with the pip freeze command\nYou should commit /share only your requirements.txt file. That is all that others and your future self need to recreate the environment.\nThe environment should be something you should throw away and be able to rebuild easily.\nMake sure to deactivate when done using it.\n\n\n\nIf you are using Conda\nChecking what is in the system:\nconda list\nTo create the environment:\nconda create --name env213\nproceed ([y]/n)? y\nTime to activate it\nconda activate env213 only work on conda 4.6 and later versions. For Conda versions prior to 4.6, run:\n\nWindows: activate or Linux and macOS: source activate\n\nYou can tell it is activated because it shows (venv213) in the prompt.\nLet’s check which packages are there with a new\nconda list env213\nEmpty, right? Nothing to worry about, it should be this way! Let’s proceed.\nInstall packages:\nWe will be installing two packages for this exercise.\nFirst:\nconda install numpy\nproceed ([y]/n)? y\nAnd then, one more package:\nconda install Pandas\nproceed ([y]/n)? y\nNow check which packages are in the specific environment we are working on:\nconda list\nAlright, the packages and dependencies installed are right there!\nExport and allow future replication of the environment:\nLet’s save the packages and dependencies we have after the installs.\nconda list --export\nThat should be stored in a environments.yml file\nSo let’s get it redirected to the required file:\nconda list -e &gt; environment.yml\nThis file should be included in your research compendium to let others reinstall your packages and dependencies as needed.\nDeactivate it:\nIf you are done with that, you should deactivate that environment by typing:\nconda deactivate\nNote: only works on conda 4.6 and later versions. For conda versions before 4.6, run:\nWindows: deactivate or Linux and macOS: source deactivate\nThen, you will see you no longer have the environment we created in our prompt.\nIf you are willing to delete the environment altogether, you should delete the directory for the virtual environment\nBack to base we can create a new environment based on the .yml packages and dependencies by running (this is noted on top of the yml file):\nconda create --name my-env --file environment.yml\nproceed ([y]/n)? y\nActivate it:\nconda activate my-env (or see above if conda version \\&lt; 4.6)\nproceed ([y]/n)? Y\nCheck if packages are there:\nconda list\nRemember to deactivate it when done:\nconda deactivate (or see above if conda version \\&lt; 4.6)\nCheck all your environments conda env list\nMore info: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html",
+    "text": "Your analysis was done with specific versions of the program used (e.g. R 4.4.3) but also of all the packages involved, as well as the specifications of the Operating System (OS) that was used. The good use is that there are tools to let you systematically capture this information.\nIn R:\nsessionInfo() or devtools::session_info() are great ways to capture this information. You should save it into a session_info.txt file to include in your GitHub repository\nIn Python:\npip freeze &gt; requirements.txt will capture all the python modules installed in your current environment",
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+    "text": "Virtual environments\nYou can even go a step further and help others to recreate the same computing environment that you used independently of what versions you have installed on your machine.\nLevel up!\n\nR\nHere is a short introduction to renv, an R package that creates virtual environment to encapsulate your R work. \nWe are going to add renv to our shorebird data cleaning project. Make sure you have the renv package installed:\ninstall.packages(\"renv\")\nYou may also choose Tools&gt;Project Options&gt;Environments and check “use renv for this project”\nOr at the R console:\nrenv::init( )\nLet’s check the new files that we have…\n\nLook at the .gitignore\nLook at the renv.lock file\n\nLet’s say we reworked our script and suppose we would need the naniar R package to deal with the NAs. Let’s update or virtual environment:\nrenv::install(\"naniar\")\nAdd some R code using this package, for example\nlibrary(naniar)\n\nsnowsurvey_csv %&gt;% \n  miss_var_summary()\nAlright, now that the installation is completed and we added this package to our code, we can save, and take a snapshot:\nrenv::snapshot()\nThis action will update the lock file, and we will see naniar and all its dependencies included.\nLet’s check. If updated, you should be good to go. If your attempts to update packages introduced new problems, you may run renv::restore() to revert to the previous state as encoded in the lockfile.\nUse .libPaths() to confirm where package installations are located!\nWant to know more? here is a good resource to get started: https://rstudio.github.io/renv/articles/renv.html\n\n\nPython\nvirtualenv is a tool to create Python virtual environments. In a nutsheel here are the steps to follow:\nCreate a new folder, your project folder. I will use EDS213. Make sure you set the path to this folder.\n\nNote: The environment will be created in the current version of Python that you are running (in Conda we can specify the version we want).\n\nTo create the environment: (second venv is the name of the environment)\npython3 -m venv venv213\n\nThe -m flag makes sure you are creating a pip that is tied to the active Python executable\n\nTime to activate it:\nsource venv213/bin/activate\nYou can tell it is activated because it shows (venv213) in the prompt.\nLet’s check which packages are there with a new pip list\nNothing, right? Only setup tools, and pip). Nothing to worry about, it should be this way! Let’s proceed.\nInstall libraries:\nWe will be installing two packages for this exercise.\nFirst:\npip install numpy\nAnd then:\npip install pandas\nThis should take a little longer!\nAnother pip list\nAlright, the packages and dependencies installed are right there!\nExport and allow future replication of the environment:\nLet’s save the packages and dependencies we have after the installs.\npip freeze\nThat should be stored in a `requirements.txt file\nSo let’s get it redirected to the required file:\npip freeze &gt; requirements.txt\nQuestion: This file won’t leave inside the venv folder, but rather in the project root folder any idea why?\nWell, you only need that file to reproduce the environment. And the venv should be should throw away and be able to rebuild easily! So, do not include any project file in that folder and treat that as disposable after the pip freeze\nTo double-check if all is good, we can run the following command:\ncat requirements.txt\nThis file should be included in your repository to let others reinstall your packages and dependencies as needed.\nDeactivate\nIf you are done with that, you should deactivate that environment by typing:\ndeactivate\nThen, you will see you no longer have the environment we created in our prompt.\nIf you are willing to delete the environment altogether, you should delete the directory for the virtual environment\nRemove folder:\nrm -rf venv213/\nReusing the Requirements\nCreate a new project folder to reuse the requirements\nmkdir my-project\nCreate a virtual env for it\npython3 -m venv my-project/venv\nActivate it\nsource my-project/venv/bin/activate\nInstall required packages\npip install -r requirements.txt\n\nAttention! Never include project files in the venv folder.\nDo not commit your venv file to the environment itself to source control (git ignore)\nYou may install more packages and update the requirements.txt with the pip freeze command\nYou should commit /share only your requirements.txt file. That is all that others and your future self need to recreate the environment.\nThe environment should be something you should throw away and be able to rebuild easily.\nMake sure to deactivate when done using it.\n\n\nIf you are using Conda\nChecking what is in the system:\nconda list\nTo create the environment:\nconda create --name env213\nproceed ([y]/n)? y\nTime to activate it\nconda activate env213 only work on conda 4.6 and later versions. For Conda versions prior to 4.6, run:\n\nWindows: activate or Linux and macOS: source activate\n\nYou can tell it is activated because it shows (venv213) in the prompt.\nLet’s check which packages are there with a new\nconda list env213\nEmpty, right? Nothing to worry about, it should be this way! Let’s proceed.\nInstall packages:\nWe will be installing two packages for this exercise.\nFirst:\nconda install numpy\nproceed ([y]/n)? y\nAnd then, one more package:\nconda install Pandas\nproceed ([y]/n)? y\nNow check which packages are in the specific environment we are working on:\nconda list\nAlright, the packages and dependencies installed are right there!\nExport and allow future replication of the environment:\nLet’s save the packages and dependencies we have after the installs.\nconda list --export\nThat should be stored in a environments.yml file\nSo let’s get it redirected to the required file:\nconda list -e &gt; environment.yml\nThis file should be included in your research compendium to let others reinstall your packages and dependencies as needed.\nDeactivate it:\nIf you are done with that, you should deactivate that environment by typing:\nconda deactivate\nNote: only works on conda 4.6 and later versions. For conda versions before 4.6, run:\nWindows: deactivate or Linux and macOS: source deactivate\nThen, you will see you no longer have the environment we created in our prompt.\nIf you are willing to delete the environment altogether, you should delete the directory for the virtual environment\nBack to base we can create a new environment based on the .yml packages and dependencies by running (this is noted on top of the yml file):\nconda create --name my-env --file environment.yml\nproceed ([y]/n)? y\nActivate it:\nconda activate my-env (or see above if conda version \\&lt; 4.6)\nproceed ([y]/n)? Y\nCheck if packages are there:\nconda list\nRemember to deactivate it when done:\nconda deactivate (or see above if conda version \\&lt; 4.6)\nCheck all your environments conda env list\nMore info: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html",
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index 2f26108..91a5cfb 100644
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