Update intro index

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-<title>DaSL Data Snacks – SSF data training</title>
+<title>R for Small-Scale Fishery Data – SSF data training</title>
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@@ -164,7 +164,7 @@
 <header id="title-block-header" class="quarto-title-block default page-columns page-full">
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-      <h1 class="title">DaSL Data Snacks</h1>
+      <h1 class="title">R for Small-Scale Fishery Data</h1>
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   </div>
 
@@ -190,8 +190,8 @@ <h5 class="quarto-listing-category-title">Categories</h5><div class="quarto-list
 
 
 
-<p>This is a collection of cool data tricks with R and Python from the <a href="https://hutchdatascience.org/">Fred Hutch Data Science Lab</a>. We leverage a technology called <code>quarto-live</code> to give you runnable R and Python examples in your browser, no installation needed. So you can immediately start experimenting with the new tips and tricks.</p>
-<p>Think of these snacks like experiments - they may grow into something bigger, or they may just stay as little nuggets of data science.</p>
+<p>This collection is designed to help you work with small-scale fishery data using R. The examples are tailored to practical use cases, leveraging <code>ggplot2</code> and other tools to analyze and visualize fisheries data. All code can be run interactively in your browser using <code>quarto-live</code>, meaning no installation is necessary to start learning and applying these techniques.</p>
+<p>These data science “snacks” are meant to provide you with hands-on experience for working with real-world fishery datasets. They can serve as quick, practical insights or as stepping stones to more advanced analysis in fisheries research.</p>
 
 
 

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@@ -135,9 +135,9 @@
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-    "text": "This is a collection of cool data tricks with R and Python from the Fred Hutch Data Science Lab. We leverage a technology called quarto-live to give you runnable R and Python examples in your browser, no installation needed. So you can immediately start experimenting with the new tips and tricks.\nThink of these snacks like experiments - they may grow into something bigger, or they may just stay as little nuggets of data science.\n\n\n\n\n\n\n   \n     \n     \n       Order By\n       Default\n         \n          Title\n        \n         \n          Date - Oldest\n        \n         \n          Date - Newest\n        \n         \n          Author\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWorking with ggplot2: A Short Guide\n\n\n\n\n\n\nR\n\n\nVisualization\n\n\nggplot2\n\n\n\n\n\n\n\n\n\nSep 30, 2024\n\n\nLore\n\n\n\n\n\n\n\n\n\n\n\n\nVisualizing Trends with ggplot2\n\n\n\n\n\n\nR\n\n\nVisualization\n\n\nTrends\n\n\n\n\n\n\n\n\n\nSep 30, 2024\n\n\nLore\n\n\n\n\n\n\nNo matching items"
+    "text": "This collection is designed to help you work with small-scale fishery data using R. The examples are tailored to practical use cases, leveraging ggplot2 and other tools to analyze and visualize fisheries data. All code can be run interactively in your browser using quarto-live, meaning no installation is necessary to start learning and applying these techniques.\nThese data science “snacks” are meant to provide you with hands-on experience for working with real-world fishery datasets. They can serve as quick, practical insights or as stepping stones to more advanced analysis in fisheries research.\n\n\n\n\n\n\n   \n     \n     \n       Order By\n       Default\n         \n          Title\n        \n         \n          Date - Oldest\n        \n         \n          Date - Newest\n        \n         \n          Author\n        \n     \n  \n    \n      \n      \n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWorking with ggplot2: A Short Guide\n\n\n\n\n\n\nR\n\n\nVisualization\n\n\nggplot2\n\n\n\n\n\n\n\n\n\nSep 30, 2024\n\n\nLore\n\n\n\n\n\n\n\n\n\n\n\n\nVisualizing Trends with ggplot2\n\n\n\n\n\n\nR\n\n\nVisualization\n\n\nTrends\n\n\n\n\n\n\n\n\n\nSep 30, 2024\n\n\nLore\n\n\n\n\n\n\nNo matching items"
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