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mod_reproducibility.html

@@ -270,8 +270,7 @@ <h2 id="toc-title">On this page</h2>
   <li><a href="#learning-objectives" id="toc-learning-objectives" class="nav-link" data-scroll-target="#learning-objectives">Learning Objectives</a></li>
   <li><a href="#module-content" id="toc-module-content" class="nav-link" data-scroll-target="#module-content">Module Content</a>
   <ul class="collapse">
-  <li><a href="#workflow-documentation" id="toc-workflow-documentation" class="nav-link" data-scroll-target="#workflow-documentation">Workflow Documentation</a></li>
-  <li><a href="#project-organization" id="toc-project-organization" class="nav-link" data-scroll-target="#project-organization">Project Organization</a></li>
+  <li><a href="#project-organization-documentation" id="toc-project-organization-documentation" class="nav-link" data-scroll-target="#project-organization-documentation">Project Organization &amp; Documentation</a></li>
   <li><a href="#reproducible-coding" id="toc-reproducible-coding" class="nav-link" data-scroll-target="#reproducible-coding">Reproducible Coding</a></li>
   <li><a href="#fair-care" id="toc-fair-care" class="nav-link" data-scroll-target="#fair-care">FAIR &amp; CARE</a></li>
   </ul></li>
@@ -317,7 +316,7 @@ <h2 class="anchored" data-anchor-id="learning-objectives">Learning Objectives</h
 <p>After completing this module you will be able to:</p>
 <ul>
 <li><u>Identify</u> core tenets of reproducibility best practices</li>
-<li><u>Create</u> reproducible workflow documentation</li>
+<li><u>Create</u> robust workflow documentation</li>
 <li><u>Implement</u> reproducible project organization strategies</li>
 <li><u>Discuss</u> methods for improving the reproducibility of your code products</li>
 <li><u>Summarize</u> FAIR and CARE data principles</li>
@@ -326,11 +325,8 @@ <h2 class="anchored" data-anchor-id="learning-objectives">Learning Objectives</h
 </section>
 <section id="module-content" class="level2">
 <h2 class="anchored" data-anchor-id="module-content">Module Content</h2>
-<section id="workflow-documentation" class="level3">
-<h3 class="anchored" data-anchor-id="workflow-documentation">Workflow Documentation</h3>
-</section>
-<section id="project-organization" class="level3">
-<h3 class="anchored" data-anchor-id="project-organization">Project Organization</h3>
+<section id="project-organization-documentation" class="level3">
+<h3 class="anchored" data-anchor-id="project-organization-documentation">Project Organization &amp; Documentation</h3>
 </section>
 <section id="reproducible-coding" class="level3">
 <h3 class="anchored" data-anchor-id="reproducible-coding">Reproducible Coding</h3>
@@ -340,14 +336,17 @@ <h3 class="anchored" data-anchor-id="fair-care">FAIR &amp; CARE</h3>
 <p>FAIR is an acronym for <u>F</u>indable <u>A</u>ccessible <u>I</u>nterpoerable and <u>R</u>eusable</p>
 <p>While making data and code more FAIR is often a good ideal the philosophy behind those four criteria come from a perspective that emphasizes data sharing as a good in and of itself. This approach can ignore historical context and contemporary power differentials and thus be insufficient as the <em>sole</em> tool to use in evaluating how data/code are shared and stored. The <a href="https://www.gida-global.org/">Global Indigenous Data Alliance</a> (GIDA) created the CARE principles with these ethical considerations explicitly built into their tenets.</p>
 <p>CARE stands for <u>C</u>ollective Benefit, <u>A</u>uthority to Control, <u>R</u>esponsibility, and <u>E</u>thics.</p>
+<p>“The CARE Principles complement, enhance and extend the FAIR Principles by advancing Indigenous self-determination and Indigenous standards to be used alongside mainstream data guidelines” – Jennings 2023</p>
 </section>
 </section>
 <section id="additional-resources" class="level2">
 <h2 class="anchored" data-anchor-id="additional-resources">Additional Resources</h2>
 <section id="papers-documents" class="level3">
 <h3 class="anchored" data-anchor-id="papers-documents">Papers &amp; Documents</h3>
 <ul>
+<li><a href="https://www.nature.com/articles/s41559-023-02161-2">Applying the ‘CARE Principles for Indigenous Data Governance’ to Ecology and Biodiversity Research</a>. Jennings <em>et al.</em>, 2023. <strong>Nature Ecology &amp; Evolution</strong></li>
 <li><a href="https://www.britishecologicalsociety.org/publications/better-science/">Guides to Better Science - Reproducible Code</a>. The British Ecological Society, 2024.</li>
+<li><a href="https://r-pkgs.org/">R Packages</a> (2nd ed.). Wickham &amp; Bryan.</li>
 </ul>
 </section>
 <section id="workshops-courses" class="level3">
@@ -364,6 +363,9 @@ <h3 class="anchored" data-anchor-id="websites">Websites</h3>
 <ul>
 <li><a href="https://nceas.github.io/scicomp.github.io/best_practices.html">Coding Tips</a>. NCEAS Scientific Computing Team, 2024.</li>
 <li><a href="https://nceas.github.io/scicomp.github.io/onboard-scaffold_team_coding.html">Team Coding: 5 Essentials</a>. NCEAS Scientific Computing Team, 2024.</li>
+<li>Advanced R (1st ed.) <a href="http://adv-r.had.co.nz/Style.html">Style Guide</a>. Wickham</li>
+<li>PEP 8 <a href="https://peps.python.org/pep-0008/">Style Guide for Python Code</a>. van Rossum <em>et al.</em> 2013.</li>
+<li><a href="https://google.github.io/styleguide/">Google Style Guides</a></li>
 </ul>
 
 

search.json

@@ -304,7 +304,7 @@
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-    "text": "Learning Objectives\nAfter completing this module you will be able to:\n\nIdentify core tenets of reproducibility best practices\nCreate reproducible workflow documentation\nImplement reproducible project organization strategies\nDiscuss methods for improving the reproducibility of your code products\nSummarize FAIR and CARE data principles\nEvaluate the FAIR/CAREness of your work",
+    "text": "Learning Objectives\nAfter completing this module you will be able to:\n\nIdentify core tenets of reproducibility best practices\nCreate robust workflow documentation\nImplement reproducible project organization strategies\nDiscuss methods for improving the reproducibility of your code products\nSummarize FAIR and CARE data principles\nEvaluate the FAIR/CAREness of your work",
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-    "text": "Module Content\n\nWorkflow Documentation\n\n\nProject Organization\n\n\nReproducible Coding\n\n\nFAIR & CARE\nFAIR is an acronym for Findable Accessible Interpoerable and Reusable\nWhile making data and code more FAIR is often a good ideal the philosophy behind those four criteria come from a perspective that emphasizes data sharing as a good in and of itself. This approach can ignore historical context and contemporary power differentials and thus be insufficient as the sole tool to use in evaluating how data/code are shared and stored. The Global Indigenous Data Alliance (GIDA) created the CARE principles with these ethical considerations explicitly built into their tenets.\nCARE stands for Collective Benefit, Authority to Control, Responsibility, and Ethics.",
+    "text": "Module Content\n\nProject Organization & Documentation\n\n\nReproducible Coding\n\n\nFAIR & CARE\nFAIR is an acronym for Findable Accessible Interpoerable and Reusable\nWhile making data and code more FAIR is often a good ideal the philosophy behind those four criteria come from a perspective that emphasizes data sharing as a good in and of itself. This approach can ignore historical context and contemporary power differentials and thus be insufficient as the sole tool to use in evaluating how data/code are shared and stored. The Global Indigenous Data Alliance (GIDA) created the CARE principles with these ethical considerations explicitly built into their tenets.\nCARE stands for Collective Benefit, Authority to Control, Responsibility, and Ethics.\n“The CARE Principles complement, enhance and extend the FAIR Principles by advancing Indigenous self-determination and Indigenous standards to be used alongside mainstream data guidelines” – Jennings 2023",
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-    "text": "Additional Resources\n\nPapers & Documents\n\nGuides to Better Science - Reproducible Code. The British Ecological Society, 2024.\n\n\n\nWorkshops & Courses\n\nData Analysis and Visualization in R for Ecologists, Episode 1: Before We Start. The Carpentries\nIntroduction to R for Geospatial Data, Episode 2: Project Management with RStudio. The Carpentries\ncoreR Course, Chapter 5: FAIR and CARE Principles. National Center for Ecological Analysis and Synthesis (NCEAS) Learning Hub, 2023.\ncoreR Course, Chapter 18: Reproducibility & Provenance. NCEAS Learning Hub, 2023.\n\n\n\nWebsites\n\nCoding Tips. NCEAS Scientific Computing Team, 2024.\nTeam Coding: 5 Essentials. NCEAS Scientific Computing Team, 2024.",
+    "text": "Additional Resources\n\nPapers & Documents\n\nApplying the ‘CARE Principles for Indigenous Data Governance’ to Ecology and Biodiversity Research. Jennings et al., 2023. Nature Ecology & Evolution\nGuides to Better Science - Reproducible Code. The British Ecological Society, 2024.\nR Packages (2nd ed.). Wickham & Bryan.\n\n\n\nWorkshops & Courses\n\nData Analysis and Visualization in R for Ecologists, Episode 1: Before We Start. The Carpentries\nIntroduction to R for Geospatial Data, Episode 2: Project Management with RStudio. The Carpentries\ncoreR Course, Chapter 5: FAIR and CARE Principles. National Center for Ecological Analysis and Synthesis (NCEAS) Learning Hub, 2023.\ncoreR Course, Chapter 18: Reproducibility & Provenance. NCEAS Learning Hub, 2023.\n\n\n\nWebsites\n\nCoding Tips. NCEAS Scientific Computing Team, 2024.\nTeam Coding: 5 Essentials. NCEAS Scientific Computing Team, 2024.\nAdvanced R (1st ed.) Style Guide. Wickham\nPEP 8 Style Guide for Python Code. van Rossum et al. 2013.\nGoogle Style Guides",
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