Built site for gh-pages

.nojekyll

@@ -1 +1 @@
-93ceadc6
+dd084e74

3-3-stabilize.html

@@ -377,7 +377,7 @@ <h3 class="anchored" data-anchor-id="what-is-a-computing-environment">What is a
 <div class="cell-output cell-output-stdout">
 <pre><code>R version 4.2.0 (2022-04-22)
 Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 22.04.4 LTS
+Running under: Ubuntu 22.04.5 LTS
 
 Matrix products: default
 BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3

3-computational-workflows.html

@@ -366,7 +366,7 @@ <h1 class="title">Computational workflows</h1>
 <p>Read this chapter and watch this week’s videos. Afterwards go through the following assignments:</p>
 <ul>
 <li>Install git</li>
-<li>Create a Git repository on GitHub or GitLab (you can call it e.g.&nbsp;“sandbox” or “just-trying-Git” :wink:). Then <code>clone</code> it, edit a file, <code>add</code>, <code>commit</code>, and <code>push</code> your changes.</li>
+<li>Create a Git repository on GitHub or GitLab (you can call it e.g.&nbsp;“sandbox” or “just-trying-Git” 😉). Then <code>clone</code> it, edit a file, <code>add</code>, <code>commit</code>, and <code>push</code> your changes.</li>
 <li>Optional: Create a Git repository for your current research project.</li>
 <li>Optional: Think of things you would like to see improved in our course booklet (Alternatively: view the <a href="https://github.com/BERD-NFDI/BERD-reproducible-research-course/issues">open issues</a>) and choose one thing where you can make a contribution. Create a <a href="https://github.com/BERD-NFDI/BERD-reproducible-research-course/fork">fork</a> of the <a href="https://github.com/BERD-NFDI/BERD-reproducible-research-course">booklet project</a> and then a <a href="https://github.com/BERD-NFDI/BERD-reproducible-research-course/pulls">pull request</a> with your contribution and mark it in the respective issue.</li>
 <li>Optional: try using Make to automate something in your current research project.</li>

search.json

@@ -136,7 +136,7 @@
     "href": "3-3-stabilize.html",
     "title": "10  Stabilize your computing environment and software",
     "section": "",
-    "text": "This topic may sound technical and boring at first, but please bare with me 🙏. It will be useful!\nHave you ever had the problem that you ran an old code and it just did not work anymore? After hours of digging into the issue you find that it’s because the software package you use has changed in the meantime 🧐\nOr have you tried to reproduce someone else’s code, which seems to run on their machine but not on yours and you just don’t know why.\nThis chapter is all about avoiding such problems in the future by stabilizing your computing environment and software. ✅\n\nWhat is a computing environment?\nYour computing environment is defined by your computer, the operating system and the software installed. If you update your operating system or your software, your computing environment changes. In R, for example, you can learn a lot about your computing environment by typing sessionInfo().\n\nsessionInfo()\n\nR version 4.2.0 (2022-04-22)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.4 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3\nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so\n\nlocale:\n [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       \n [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   \n [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          \n[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   \n\nattached base packages:\n[1] stats     graphics  grDevices datasets  utils     methods   base     \n\nloaded via a namespace (and not attached):\n [1] compiler_4.2.0  fastmap_1.1.1   cli_3.6.1       htmltools_0.5.5\n [5] tools_4.2.0     yaml_2.3.7      rmarkdown_2.21  knitr_1.42     \n [9] jsonlite_1.8.4  xfun_0.39       digest_0.6.31   rlang_1.1.1    \n[13] renv_1.0.0      evaluate_0.20  \n\n\nIt tells the R version, operating system, loaded R packages as well as their versions.\n\n\nOptions for stabilizing your computing environment\n\n1) Record your computing environment\nDocument the software versions you used. For example if you use R, you could copy the output of sessionInfo() into your README or somewhere else where future you (and others) can find this information. This is not exactly “stabilizing” but it gives the possibility to install the same software versions again.\n\n\n2) Use one virtual machine per research project\nYou don’t need to know what a virtual machine is or how to set it up to be able to do this. I used to ask the wonderful IT person at my institute to set up a virtual machine for me and if your IT supporters know their job, they’ll be able to help you here.\nA virtual machine is essentially a virtual computer on another computer or server (To those nerds out there, I know I am probably explaining it incorrectly but for the purpose of what we want to achieve here, it’s good enough). If you have one virtual machine for each project, you can keep the computing environment stable by not installing or updating software after you’ve finished the research project.\nThe downside of this strategy is that this is only for future you and your collaborators, but not for other researchers who want to work with the same computing environment.\n\n\n3) Use one container per research project\nContainers are similar to virtual machines (think little computer inside your computer). The big difference is that you can make them available for others. So you can send your container image (or the file describing it) to others.\n\n\n\nPopular container tools are Docker and Apptainer (formerly Singularity). Learning to work with containers is not super easy, but it is worth the time and actually can be applied in so many other situations. So, a great skill to have even if you decide to quit research.\n\n\n4) Other\nThere are many other options out there. I wrote down the three that are least dependent on the actual software you use. For R users, check out packages logrx, rang, packrat, versions, and renv.\n\n\n\nFurther reading\n\nReproducible Environments, The Turing Way\nVideo: How can software containers help your research?, Paula Andrea Martinez + Australian Research Data Commons\nR Docker tutorial, maintained by Jemma Stachelek\n\nThat’s all for this chapter. I hope it was helpful and not too technical. Happy researching! 🙌",
+    "text": "This topic may sound technical and boring at first, but please bare with me 🙏. It will be useful!\nHave you ever had the problem that you ran an old code and it just did not work anymore? After hours of digging into the issue you find that it’s because the software package you use has changed in the meantime 🧐\nOr have you tried to reproduce someone else’s code, which seems to run on their machine but not on yours and you just don’t know why.\nThis chapter is all about avoiding such problems in the future by stabilizing your computing environment and software. ✅\n\nWhat is a computing environment?\nYour computing environment is defined by your computer, the operating system and the software installed. If you update your operating system or your software, your computing environment changes. In R, for example, you can learn a lot about your computing environment by typing sessionInfo().\n\nsessionInfo()\n\nR version 4.2.0 (2022-04-22)\nPlatform: x86_64-pc-linux-gnu (64-bit)\nRunning under: Ubuntu 22.04.5 LTS\n\nMatrix products: default\nBLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3\nLAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so\n\nlocale:\n [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       \n [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   \n [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          \n[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   \n\nattached base packages:\n[1] stats     graphics  grDevices datasets  utils     methods   base     \n\nloaded via a namespace (and not attached):\n [1] compiler_4.2.0  fastmap_1.1.1   cli_3.6.1       htmltools_0.5.5\n [5] tools_4.2.0     yaml_2.3.7      rmarkdown_2.21  knitr_1.42     \n [9] jsonlite_1.8.4  xfun_0.39       digest_0.6.31   rlang_1.1.1    \n[13] renv_1.0.0      evaluate_0.20  \n\n\nIt tells the R version, operating system, loaded R packages as well as their versions.\n\n\nOptions for stabilizing your computing environment\n\n1) Record your computing environment\nDocument the software versions you used. For example if you use R, you could copy the output of sessionInfo() into your README or somewhere else where future you (and others) can find this information. This is not exactly “stabilizing” but it gives the possibility to install the same software versions again.\n\n\n2) Use one virtual machine per research project\nYou don’t need to know what a virtual machine is or how to set it up to be able to do this. I used to ask the wonderful IT person at my institute to set up a virtual machine for me and if your IT supporters know their job, they’ll be able to help you here.\nA virtual machine is essentially a virtual computer on another computer or server (To those nerds out there, I know I am probably explaining it incorrectly but for the purpose of what we want to achieve here, it’s good enough). If you have one virtual machine for each project, you can keep the computing environment stable by not installing or updating software after you’ve finished the research project.\nThe downside of this strategy is that this is only for future you and your collaborators, but not for other researchers who want to work with the same computing environment.\n\n\n3) Use one container per research project\nContainers are similar to virtual machines (think little computer inside your computer). The big difference is that you can make them available for others. So you can send your container image (or the file describing it) to others.\n\n\n\nPopular container tools are Docker and Apptainer (formerly Singularity). Learning to work with containers is not super easy, but it is worth the time and actually can be applied in so many other situations. So, a great skill to have even if you decide to quit research.\n\n\n4) Other\nThere are many other options out there. I wrote down the three that are least dependent on the actual software you use. For R users, check out packages logrx, rang, packrat, versions, and renv.\n\n\n\nFurther reading\n\nReproducible Environments, The Turing Way\nVideo: How can software containers help your research?, Paula Andrea Martinez + Australian Research Data Commons\nR Docker tutorial, maintained by Jemma Stachelek\n\nThat’s all for this chapter. I hope it was helpful and not too technical. Happy researching! 🙌",
     "crumbs": [
       "Computational workflows",
       "<span class='chapter-number'>10</span>  <span class='chapter-title'>Stabilize your computing environment and software</span>"
@@ -276,7 +276,7 @@
     "href": "3-computational-workflows.html",
     "title": "Computational workflows",
     "section": "",
-    "text": "Learning targets\n\n\n\n\nYou will have the version control system git installed on your computer and be able to run the important commands.\nYou will be able to keep your computing environment stable.\nYou will have a roadmap on how to automate your code.\n\n\n\n\n\n\n\n\n\nImportant\n\n\n\nThis week’s assignments are a bit more difficult and will take longer to implement that the previous. Please plan for this.\n\n\n\n\n\n\n\n\nTasks\n\n\n\nRead this chapter and watch this week’s videos. Afterwards go through the following assignments:\n\nInstall git\nCreate a Git repository on GitHub or GitLab (you can call it e.g. “sandbox” or “just-trying-Git” :wink:). Then clone it, edit a file, add, commit, and push your changes.\nOptional: Create a Git repository for your current research project.\nOptional: Think of things you would like to see improved in our course booklet (Alternatively: view the open issues) and choose one thing where you can make a contribution. Create a fork of the booklet project and then a pull request with your contribution and mark it in the respective issue.\nOptional: try using Make to automate something in your current research project.\n\nDiscuss your progress with your accountability buddy.",
+    "text": "Learning targets\n\n\n\n\nYou will have the version control system git installed on your computer and be able to run the important commands.\nYou will be able to keep your computing environment stable.\nYou will have a roadmap on how to automate your code.\n\n\n\n\n\n\n\n\n\nImportant\n\n\n\nThis week’s assignments are a bit more difficult and will take longer to implement that the previous. Please plan for this.\n\n\n\n\n\n\n\n\nTasks\n\n\n\nRead this chapter and watch this week’s videos. Afterwards go through the following assignments:\n\nInstall git\nCreate a Git repository on GitHub or GitLab (you can call it e.g. “sandbox” or “just-trying-Git” 😉). Then clone it, edit a file, add, commit, and push your changes.\nOptional: Create a Git repository for your current research project.\nOptional: Think of things you would like to see improved in our course booklet (Alternatively: view the open issues) and choose one thing where you can make a contribution. Create a fork of the booklet project and then a pull request with your contribution and mark it in the respective issue.\nOptional: try using Make to automate something in your current research project.\n\nDiscuss your progress with your accountability buddy.",
     "crumbs": [
       "Computational workflows"
     ]