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--- a/.nojekyll
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
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   <div class="sidebar-item-container"> 
+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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+  <div class="sidebar-item-container"> 
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  <span class="menu-text">US College Scorecard</span></a>
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
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   <div class="sidebar-item-container"> 
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
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   <div class="sidebar-item-container"> 
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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diff --git a/biology/anage.html b/biology/anage.html
index 3ed5c3e..9da1547 100644
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
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   <div class="sidebar-item-container"> 
+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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+  <div class="sidebar-item-container"> 
   <a href="../daily-life/college-scorecard.html" class="sidebar-item-text sidebar-link">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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   <div class="sidebar-item-container"> 
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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   <div class="sidebar-item-container"> 
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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   <div class="sidebar-item-container"> 
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diff --git a/biology/bird-diversity.html b/biology/bird-diversity.html
index 4a1012b..e88cc2b 100644
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
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       <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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+  <div class="sidebar-item-container"> 
   <a href="../daily-life/college-scorecard.html" class="sidebar-item-text sidebar-link">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
  <span class="menu-text">Medicine</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="../medicine/bloodletting.html" class="sidebar-item-text sidebar-link">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="../money/ames-housing.html" class="sidebar-item-text sidebar-link">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="../politics/mapping-police-violence.html" class="sidebar-item-text sidebar-link">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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+ <span class="menu-text">Traffic stops in Connecticut</span></a>
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+ <span class="menu-text">Traffic stops in Connecticut</span></a>
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+<div class="body">
+<a href="./crime-and-justice/connecticut-stops.html">
+<h3 class="no-anchor listing-title">
+Traffic stops in Connecticut
+</h3>
+<div class="listing-subtitle">
+
+</div>
+<div class="listing-description">
+<p>Records for every traffic stop made by the Connecticut State Police over several years, including the reason for the stop and demographic details. Use classifiers to study whether stop and search decisions show signs of bias.</p>
+</div>
+</a>
+</div>
+<div class="metadata">
+<a href="./crime-and-justice/connecticut-stops.html">
+<div class="listing-date">
+Feb 9, 2019
+</div>
+<div class="listing-author">
+Alex Reinhart
+</div>
+</a>
+</div>
+</div>
+<div class="quarto-post image-right" data-index="22" data-categories="linear regression,ANOVA" data-listing-date-sort="1691452800000" data-listing-file-modified-sort="1694112438492" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="5">
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diff --git a/crime-and-justice/connecticut-stops.html b/crime-and-justice/connecticut-stops.html
new file mode 100644
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+<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
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+<meta name="author" content="Alex Reinhart">
+<meta name="dcterms.date" content="2019-02-09">
+<meta name="description" content="Records for every traffic stop made by the Connecticut State Police over several years, including the reason for the stop and demographic details. Use classifiers to study whether stop and search decisions show signs of bias.">
+
+<title>CMU S&amp;DS Data Repository - Traffic stops in Connecticut</title>
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+ <span class="menu-text">Traffic stops in Connecticut</span></a>
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+   
+  <ul>
+  <li><a href="#motivation" id="toc-motivation" class="nav-link active" data-scroll-target="#motivation">Motivation</a></li>
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+  <ul class="collapse">
+  <li><a href="#data-preview" id="toc-data-preview" class="nav-link" data-scroll-target="#data-preview">Data preview</a></li>
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+  </ul></li>
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+  </ul>
+<div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/cmustatistics/data-repository/edit/main/crime-and-justice/connecticut-stops.qmd" class="toc-action">Edit this page</a></p><p><a href="https://github.com/cmustatistics/data-repository/issues/new" class="toc-action">Report an issue</a></p></div></div></nav>
+    </div>
+<!-- main -->
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">Traffic stops in Connecticut</h1>
+  <div class="quarto-categories">
+    <div class="quarto-category">classification</div>
+    <div class="quarto-category">logistic regression</div>
+  </div>
+  </div>
+
+<div>
+  <div class="description">
+    Records for every traffic stop made by the Connecticut State Police over several years, including the reason for the stop and demographic details. Use classifiers to study whether stop and search decisions show signs of bias.
+  </div>
+</div>
+
+
+<div class="quarto-title-meta">
+
+    <div>
+    <div class="quarto-title-meta-heading">Author</div>
+    <div class="quarto-title-meta-contents">
+             <p>Alex Reinhart </p>
+          </div>
+  </div>
+  
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">February 9, 2019</p>
+    </div>
+  </div>
+  
+  
+  
+    <div>
+    <div class="quarto-title-meta-heading">Data files</div>
+    <div class="quarto-title-meta-contents">
+            • <a class="data-file" href="https://cmustatistics.github.io/data-repository/data/connecticut-stops.csv.gz">connecticut-stops.csv.gz</a>
+          </div>
+  </div>
+  
+    <div>
+    <div class="quarto-title-meta-heading">Data year</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">2015</p>
+    </div>
+  </div>
+  </div>
+
+
+</header>
+
+<section id="motivation" class="level2">
+<h2 class="anchored" data-anchor-id="motivation">Motivation</h2>
+<p>In the United States, police officers routinely make traffic stops for a variety of reasons, like speeding, reckless driving, or use of cell phones while driving. They’re one of the main ways ordinary people interact with police, and while most traffic stops are completely routine, others are more serious, leading to searches, arrests, and sometimes violence.</p>
+<p>Because traffic stops are so common, they’re also widely scrutinized. Some allege that officers are racially biased in who they stop, which vehicles they choose to search, or who they decide to give tickets instead of warnings to. Until recent years, detailed data was not available to help answer these allegations, but new initiatives have begun to collect extensive data from police departments across many states.</p>
+</section>
+<section id="data" class="level2">
+<h2 class="anchored" data-anchor-id="data">Data</h2>
+<p>This dataset records all traffic stops made by the Connecticut State Police (not the police departments of individual towns and cities) from October 2013 to March 2015, amounting to nearly 320,000 traffic stops. Each row is one stop.</p>
+<p>The data summarizes each stop, including driver demographics, the reason for the stop, and whether a search was conducted or contraband was found.</p>
+<section id="data-preview" class="level3">
+<h3 class="anchored" data-anchor-id="data-preview">Data preview</h3>
+<h4 class="anchored">
+<a href=" https://cmustatistics.github.io/data-repository/data/connecticut-stops.csv.gz "> connecticut-stops.csv.gz </a>
+</h4>
+<div data-pagedtable="false">
+
+<script data-pagedtable-source="" type="application/json">
+{"columns":[{"label":["id"],"name":[1],"type":["chr"],"align":["left"]},{"label":["state"],"name":[2],"type":["chr"],"align":["left"]},{"label":["stop_date"],"name":[3],"type":["date"],"align":["right"]},{"label":["stop_time"],"name":[4],"type":["time"],"align":["right"]},{"label":["location_raw"],"name":[5],"type":["chr"],"align":["left"]},{"label":["county_name"],"name":[6],"type":["chr"],"align":["left"]},{"label":["county_fips"],"name":[7],"type":["chr"],"align":["left"]},{"label":["fine_grained_location"],"name":[8],"type":["chr"],"align":["left"]},{"label":["police_department"],"name":[9],"type":["chr"],"align":["left"]},{"label":["driver_gender"],"name":[10],"type":["chr"],"align":["left"]},{"label":["driver_age_raw"],"name":[11],"type":["dbl"],"align":["right"]},{"label":["driver_age"],"name":[12],"type":["dbl"],"align":["right"]},{"label":["driver_race_raw"],"name":[13],"type":["chr"],"align":["left"]},{"label":["driver_race"],"name":[14],"type":["chr"],"align":["left"]},{"label":["violation_raw"],"name":[15],"type":["chr"],"align":["left"]},{"label":["violation"],"name":[16],"type":["chr"],"align":["left"]},{"label":["search_conducted"],"name":[17],"type":["lgl"],"align":["right"]},{"label":["search_type_raw"],"name":[18],"type":["chr"],"align":["left"]},{"label":["search_type"],"name":[19],"type":["chr"],"align":["left"]},{"label":["contraband_found"],"name":[20],"type":["lgl"],"align":["right"]},{"label":["stop_outcome"],"name":[21],"type":["chr"],"align":["left"]},{"label":["is_arrested"],"name":[22],"type":["lgl"],"align":["right"]},{"label":["officer_id"],"name":[23],"type":["chr"],"align":["left"]},{"label":["stop_duration"],"name":[24],"type":["chr"],"align":["left"]}],"data":[{"1":"CT-2013-00001","2":"CT","3":"2013-10-01","4":"00:01:00","5":"westport","6":"Fairfield 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min"},{"1":"CT-2013-00009","2":"CT","3":"2013-10-01","4":"00:15:00","5":"manchester","6":"Hartford County","7":"09003","8":"00000 S WILBUR CROSS HWY (MANCHESTER, T077)","9":"State Police","10":"M","11":"25","12":"25","13":"White","14":"White","15":"Speed Related","16":"Speeding","17":"FALSE","18":"NA","19":"NA","20":"FALSE","21":"Ticket","22":"FALSE","23":"1000001920","24":"1-15 min"},{"1":"CT-2013-00010","2":"CT","3":"2013-10-01","4":"00:16:00","5":"beacon falls","6":"New Haven County","7":"09009","8":"rt 8 exit 25","9":"State Police","10":"M","11":"32","12":"32","13":"White","14":"White","15":"Moving Violation","16":"Moving violation","17":"FALSE","18":"NA","19":"NA","20":"FALSE","21":"Verbal Warning","22":"FALSE","23":"1000003026","24":"1-15 min"},{"1":"CT-2013-00011","2":"CT","3":"2013-10-01","4":"00:20:00","5":"westport","6":"Fairfield County","7":"09001","8":"00000 S I 95 (WESTPORT, T158) X18 LL","9":"State 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Police","10":"F","11":"21","12":"21","13":"Black","14":"Black","15":"Other","16":"Other","17":"FALSE","18":"NA","19":"NA","20":"FALSE","21":"Ticket","22":"FALSE","23":"1000001903","24":"1-15 min"},{"1":"CT-2013-00017","2":"CT","3":"2013-10-01","4":"00:28:00","5":"waterbury","6":"New Haven County","7":"09009","8":"Rt 8 south, exit 30","9":"State Police","10":"M","11":"67","12":"67","13":"White","14":"White","15":"Other","16":"Other","17":"FALSE","18":"NA","19":"NA","20":"FALSE","21":"Verbal Warning","22":"FALSE","23":"1000002230","24":"1-15 min"},{"1":"CT-2013-00018","2":"CT","3":"2013-10-01","4":"00:29:00","5":"union","6":"Tolland County","7":"09013","8":"x73","9":"State Police","10":"M","11":"42","12":"42","13":"Hispanic","14":"Hispanic","15":"Other","16":"Other","17":"FALSE","18":"NA","19":"NA","20":"FALSE","21":"Verbal Warning","22":"FALSE","23":"1000002715","24":"1-15 min"},{"1":"CT-2013-00019","2":"CT","3":"2013-10-01","4":"00:30:00","5":"windham","6":"Windham County","7":"09015","8":"rt 203","9":"State Police","10":"M","11":"21","12":"21","13":"White","14":"White","15":"Speed Related","16":"Speeding","17":"FALSE","18":"NA","19":"NA","20":"FALSE","21":"Verbal Warning","22":"FALSE","23":"1000002591","24":"1-15 min"},{"1":"CT-2013-00020","2":"CT","3":"2013-10-01","4":"00:34:00","5":"hebron","6":"Tolland County","7":"09013","8":"rt 66","9":"State Police","10":"M","11":"23","12":"23","13":"White","14":"White","15":"Other","16":"Other","17":"FALSE","18":"NA","19":"NA","20":"FALSE","21":"Ticket","22":"FALSE","23":"1000002242","24":"1-15 min"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
+  </script>
+</div>
+</section>
+<section id="variable-descriptions" class="level3">
+<h3 class="anchored" data-anchor-id="variable-descriptions">Variable descriptions</h3>
+<table class="table">
+<colgroup>
+<col style="width: 23%">
+<col style="width: 76%">
+</colgroup>
+<thead>
+<tr class="header">
+<th>Variable</th>
+<th>Description</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>id</td>
+<td>A unique ID assigned to each stop.</td>
+</tr>
+<tr class="even">
+<td>state</td>
+<td>The state in which each stop occurred (always CT, for Connecticut, in this data)</td>
+</tr>
+<tr class="odd">
+<td>stop_date</td>
+<td>Date of the traffic stop (YYYY-MM-DD)</td>
+</tr>
+<tr class="even">
+<td>stop_time</td>
+<td>Time the stop occurred (24-hour time, HH:MM)</td>
+</tr>
+<tr class="odd">
+<td>location_raw</td>
+<td>Location name from original records, used to identify the county</td>
+</tr>
+<tr class="even">
+<td>county_name</td>
+<td>County in which the stop occurred</td>
+</tr>
+<tr class="odd">
+<td>county_fips</td>
+<td>The unique FIPS ID code for the county</td>
+</tr>
+<tr class="even">
+<td>fine_grained_location</td>
+<td>A more detailed location, not in a standardized format</td>
+</tr>
+<tr class="odd">
+<td>police_department</td>
+<td>Name of the police department making the stop</td>
+</tr>
+<tr class="even">
+<td>driver_gender</td>
+<td>Gender of the driver, as recorded by the officer making the stop</td>
+</tr>
+<tr class="odd">
+<td>driver_age_raw</td>
+<td>Driver’s age from the original dataset. Not always in a consistent format</td>
+</tr>
+<tr class="even">
+<td>driver_age</td>
+<td>Driver’s age; NA if under 15 or greater than 99</td>
+</tr>
+<tr class="odd">
+<td>driver_race_raw</td>
+<td>The driver’s race, as recorded by the officer. Not in a standard format.</td>
+</tr>
+<tr class="even">
+<td>driver_race</td>
+<td>The driver’s race, standardized to these options: White, Black, Hispanic, Asian, Other, and NA (unknown). Asian includes Asian, Pacific Islander, and Indian. Native Americans are listed as Other, and anyone with Hispanic ethnicity is listed as Hispanic.</td>
+</tr>
+<tr class="odd">
+<td>violation_raw</td>
+<td>The violation for which the driver was stopped, from the original data</td>
+</tr>
+<tr class="even">
+<td>violation</td>
+<td>The violation broken into standard categories, like Speeding, Lights, or Moving violation. Some stops involve multiple violations, separated by commas.</td>
+</tr>
+<tr class="odd">
+<td>search_conducted</td>
+<td>TRUE if the officer searched the vehicle; FALSE otherwise.</td>
+</tr>
+<tr class="even">
+<td>search_type_raw</td>
+<td>Justification for the search, if conducted, as written by the police agency</td>
+</tr>
+<tr class="odd">
+<td>search_type</td>
+<td>Justification for the search broken down into consistent categories like Consent (driver gave consent). Many are listed as Other with no reason given</td>
+</tr>
+<tr class="even">
+<td>contraband_found</td>
+<td>Whether the search found contraband (like drugs). TRUE or FALSE, and FALSE if no search was performed.</td>
+</tr>
+<tr class="odd">
+<td>stop_outcome</td>
+<td>The result of the stop: arrest, ticket, warning, and so on.</td>
+</tr>
+<tr class="even">
+<td>is_arrested</td>
+<td>Whether an arrest was made (TRUE or FALSE).</td>
+</tr>
+<tr class="odd">
+<td>officer_id</td>
+<td>A unique ID for the officer making the stop.</td>
+</tr>
+<tr class="even">
+<td>stop_duration</td>
+<td>Duration of the stop, broken into categories (1-5 min, 16-30 min, and so on).</td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="questions" class="level2">
+<h2 class="anchored" data-anchor-id="questions">Questions</h2>
+<ol type="1">
+<li><p>Conduct an exploratory analysis of the data. What stop types are most common? Is there variation by race or gender? What types of stop are most likely to lead to a ticket?</p></li>
+<li><p>One way to look for racial discrimination in the decision to search a vehicle is called the “outcome test”. If officers do not discriminate, and instead use only objective evidence to decide a car should be searched for drugs, then the rate at which they find drugs should be the same across racial groups. But if officers unfairly target a racial group for searches, they may conduct more searches of drivers in that group without finding any drugs.</p>
+<p>Build a model to predict whether contraband is found after a search is conducted, using race and other appropriate covariates. Does the outcome test appear to show discrimination? Carefully explain the caveats of your results, discussing confounding variables and mentioning other types of data you would like to have.</p></li>
+<li><p>Is there any evidence that traffic stops are most common certain times of year or on certain days of the week? Before conducting your analysis, make some hypotheses about what you expect to find. (Do you think officers would be less likely to make traffic stops in the cold winter months, for example?)</p></li>
+</ol>
+</section>
+<section id="references" class="level2">
+<h2 class="anchored" data-anchor-id="references">References</h2>
+<p>Data collected and cleaned by the <a href="https://openpolicing.stanford.edu/">Stanford Open Policing Project</a>, released under the <a href="https://opendatacommons.org/licenses/by/summary/">Open Data Commons Attribution License</a>. Code and data descriptions <a href="https://github.com/5harad/openpolicing">available on GitHub</a>. The Open Policing Project has released similar data files for many other states.</p>
+<p>For a rigorous analysis of this data, see E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, D. Jenson, A. Shoemaker, V. Ramachandran, P. Barghouty, C. Phillips, R. Shroff, and S. Goel. (2020) “A large-scale analysis of racial disparities in police stops across the United States”. <em>Nature Human Behavior</em> 4, 736-745. <a href="https://doi.org/10.1038/s41562-020-0858-1" class="uri">https://doi.org/10.1038/s41562-020-0858-1</a></p>
+
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index 63c4304..c7bf992 100644
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+++ b/daily-life/college-scorecard.html
@@ -203,13 +203,30 @@
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true">
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@@ -284,13 +301,13 @@
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@@ -354,13 +371,13 @@
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diff --git a/daily-life/rtg-tinned-fish.html b/daily-life/rtg-tinned-fish.html
index e7ec502..711054b 100644
--- a/daily-life/rtg-tinned-fish.html
+++ b/daily-life/rtg-tinned-fish.html
@@ -203,13 +203,30 @@
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+ <span class="menu-text">Crime and Justice</span></a>
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+</li>
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@@ -226,13 +243,13 @@
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@@ -255,13 +272,13 @@
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="../politics/mapping-police-violence.html" class="sidebar-item-text sidebar-link">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="../psychology/cmu-sleep.html" class="sidebar-item-text sidebar-link">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-9" class="collapse list-unstyled sidebar-section depth1 ">  
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diff --git a/external-sources.html b/external-sources.html
index bf42da8..0976180 100644
--- a/external-sources.html
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@@ -198,7 +198,7 @@
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
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       <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="./crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="./daily-life/college-scorecard.html" class="sidebar-item-text sidebar-link">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
  <span class="menu-text">Medicine</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="./medicine/bloodletting.html" class="sidebar-item-text sidebar-link">
@@ -249,13 +266,13 @@
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         <li class="sidebar-item sidebar-item-section">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="./money/ames-housing.html" class="sidebar-item-text sidebar-link">
@@ -278,13 +295,13 @@
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="./politics/mapping-police-violence.html" class="sidebar-item-text sidebar-link">
@@ -301,13 +318,13 @@
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="./psychology/cmu-sleep.html" class="sidebar-item-text sidebar-link">
@@ -348,13 +365,13 @@
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-9" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="./social/science-forums.html" class="sidebar-item-text sidebar-link">
diff --git a/index.html b/index.html
index dd439f9..1129d43 100644
--- a/index.html
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@@ -198,7 +198,7 @@
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       <div class="sidebar-item-container"> 
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="false" aria-label="Toggle section">
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@@ -206,6 +206,23 @@
       <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="./crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="./daily-life/college-scorecard.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">US College Scorecard</span></a>
   </div>
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
  <span class="menu-text">Medicine</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="./medicine/bloodletting.html" class="sidebar-item-text sidebar-link">
@@ -249,13 +266,13 @@
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./money/ames-housing.html" class="sidebar-item-text sidebar-link">
@@ -278,13 +295,13 @@
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         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="./politics/mapping-police-violence.html" class="sidebar-item-text sidebar-link">
@@ -301,13 +318,13 @@
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         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="./psychology/cmu-sleep.html" class="sidebar-item-text sidebar-link">
@@ -348,13 +365,13 @@
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       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-9" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="./social/science-forums.html" class="sidebar-item-text sidebar-link">
diff --git a/listings.json b/listings.json
index b1b4e44..b575ef3 100644
--- a/listings.json
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@@ -23,6 +23,7 @@
       "/biology/spider-nuptials.html",
       "/psychology/sugary-drinks.html",
       "/psychology/hormone-diversity.html",
+      "/crime-and-justice/connecticut-stops.html",
       "/daily-life/college-scorecard.html",
       "/politics/world-bank.html"
     ]
diff --git a/medicine/bloodletting.html b/medicine/bloodletting.html
index 6b4df86..07997d5 100644
--- a/medicine/bloodletting.html
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       <div class="sidebar-item-container"> 
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="false" aria-label="Toggle section">
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@@ -214,6 +214,23 @@
       <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../daily-life/college-scorecard.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">US College Scorecard</span></a>
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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+            <i class="bi bi-chevron-right ms-2"></i>
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
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+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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+  <div class="sidebar-item-container"> 
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="true">
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-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="true" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
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+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="true" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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diff --git a/money/rail-trails.html b/money/rail-trails.html
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- <span class="menu-text">Daily Life</span></a>
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+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
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+  </li>
+        <li class="sidebar-item sidebar-item-section">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
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+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="true">
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-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="true" aria-label="Toggle section">
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           </a> 
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-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 show">  
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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index c1fa430..8e9e74c 100644
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+ <span class="menu-text">Crime and Justice</span></a>
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+ <span class="menu-text">Traffic stops in Connecticut</span></a>
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="true" aria-label="Toggle section">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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index 8776e07..7182baf 100644
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+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
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+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="true" aria-label="Toggle section">
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+ <span class="menu-text">Traffic stops in Connecticut</span></a>
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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+ <span class="menu-text">Traffic stops in Connecticut</span></a>
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@@ -307,13 +324,13 @@
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-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="true">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="true">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="true" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 show">  
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   <div class="sidebar-item-container"> 
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@@ -354,13 +371,13 @@
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-9" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
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diff --git a/psychology/religion-analytic-thinking.html b/psychology/religion-analytic-thinking.html
index a4a27a6..48cf652 100644
--- a/psychology/religion-analytic-thinking.html
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             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="false">
- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
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       <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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+  <div class="sidebar-item-container"> 
   <a href="../daily-life/college-scorecard.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">US College Scorecard</span></a>
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         <li class="sidebar-item sidebar-item-section">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
  <span class="menu-text">Medicine</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
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   <div class="sidebar-item-container"> 
   <a href="../medicine/bloodletting.html" class="sidebar-item-text sidebar-link">
@@ -255,13 +272,13 @@
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         <li class="sidebar-item sidebar-item-section">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
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           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../money/ames-housing.html" class="sidebar-item-text sidebar-link">
@@ -284,13 +301,13 @@
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         <li class="sidebar-item sidebar-item-section">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../politics/mapping-police-violence.html" class="sidebar-item-text sidebar-link">
@@ -307,13 +324,13 @@
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="true">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="true">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../psychology/cmu-sleep.html" class="sidebar-item-text sidebar-link">
@@ -354,13 +371,13 @@
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-9" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../social/science-forums.html" class="sidebar-item-text sidebar-link">
diff --git a/psychology/sugary-drinks.html b/psychology/sugary-drinks.html
index 5bb07ab..898c1b1 100644
--- a/psychology/sugary-drinks.html
+++ b/psychology/sugary-drinks.html
@@ -204,7 +204,7 @@
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       <div class="sidebar-item-container"> 
             <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="false">
- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="false" aria-label="Toggle section">
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@@ -212,6 +212,23 @@
       <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="../crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="../daily-life/college-scorecard.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">US College Scorecard</span></a>
   </div>
@@ -226,13 +243,13 @@
   </li>
         <li class="sidebar-item sidebar-item-section">
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
  <span class="menu-text">Medicine</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../medicine/bloodletting.html" class="sidebar-item-text sidebar-link">
@@ -255,13 +272,13 @@
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
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           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../money/ames-housing.html" class="sidebar-item-text sidebar-link">
@@ -284,13 +301,13 @@
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../politics/mapping-police-violence.html" class="sidebar-item-text sidebar-link">
@@ -307,13 +324,13 @@
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="true">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="true">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../psychology/cmu-sleep.html" class="sidebar-item-text sidebar-link">
@@ -354,13 +371,13 @@
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-9" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
   <a href="../social/science-forums.html" class="sidebar-item-text sidebar-link">
diff --git a/search.json b/search.json
index 09056fa..b7a1166 100644
--- a/search.json
+++ b/search.json
@@ -4,7 +4,7 @@
     "href": "by-method.html",
     "title": "All Datasets",
     "section": "",
-    "text": "All datasets are listed below, and can be filtered by statistical method on the right. A downloadable spreadsheet of all datasets is also available for more advanced searches.\n\n\n\n\n\n\n\n\n\n\nAnAge Longevity Database\n\n\n\n\n\nThe AnAge database includes information on the longevity of thousands of animal species, allowing studies of the factors related to long lifespans.\n\n\n\n\n\n\nDec 27, 2021\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nBird genetic diversity\n\n\n\n\n\nEvolutionary theory suggests that species spread over a wider geographic range should have greater genetic diversity. Does this hold true in birds? An observational study of 387 bird species.\n\n\n\n\n\n\nJul 10, 2019\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nBloodletting\n\n\n\n\n\nBloodletting—deliberately withdrawing large amounts of blood from patients—was a common medical practice for centuries, until evidence was finally collected on its efficacy. Use an early observational dataset to explore its use for pneumonia.\n\n\n\n\n\n\nJun 8, 2023\n\n\nGordon Weinberg\n\n\n\n\n\n\n\n\nCore temperature during surgery\n\n\n\n\n\nIs low core temperature during surgery associated with poor surgical outcomes or death? Use logistic and survival analysis to study an observational dataset.\n\n\n\n\n\n\nMay 17, 2023\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nDry beans\n\n\n\n\n\nSort and classify beans using features extracted from images.\n\n\n\n\n\n\nDec 27, 2021\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nEuropean protected ham\n\n\n\n\n\nIn Europe, some types of ham—like Black Forest ham—can only be legally produced in specific geographic regions. Does the size of those regions affect the price of the ham?\n\n\n\n\n\n\nJun 9, 2023\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nGalaxy mass prediction\n\n\n\n\n\nGalaxies span a range of masses. Can students learn a statistical model that associates a galaxy’s mass with its brightness as measured at six wavelengths along with an estimate of its distance from the Earth?\n\n\n\n\n\n\nFeb 3, 2023\n\n\nPeter Freeman\n\n\n\n\n\n\n\n\nHealth exams in Vietnam\n\n\n\n\n\nAs access to health care increases, public health advocates encourage ordinary people to get regular check-ups to detect problems before they become serious (and expensive). But not everyone follows this advice. This survey of people in Vietnam explores the factors leading people to get regular check-ups.\n\n\n\n\n\n\nDec 8, 2022\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nHouse prices in Ames, Iowa\n\n\n\n\n\nRecords of homes sold in Ames, Iowa, and their sale prices, including detailed covariates that could be used to predict home prices.\n\n\n\n\n\n\nAug 14, 2019\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nInsect attraction to light\n\n\n\n\n\nWhat kinds of lights are insects most attracted to? Analyze the results of a factorial experiment using light traps.\n\n\n\n\n\n\nDec 8, 2022\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nMakeup and male attractiveness\n\n\n\n\n\nMakeup is often used by women, but does makeup make men more attractive? This study had participants rate photos of men with and without makeup on.\n\n\n\n\n\n\nNov 4, 2022\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nMapping Police Violence\n\n\n\n\n\nEach year in the United States, around 1,000 people are killed by police. Some of these deaths are accidental, in traffic accidents and other incidents; some are deliberate killings considered legally justified by authorities; and others are considered unjustified or even lead to the prosecution of the police officer. Explore data on over 10,000 such killings to identify patterns in the people killed, the stated reasons for their killings, and the situations leading to their deaths.\n\n\n\n\n\n\nSep 6, 2023\n\n\nJessica Zhiyu Guo\n\n\n\n\n\n\n\n\nNightly sleep time and GPA in first-years\n\n\n\n\n\nDoes nightly sleep duration predict change in end-of-semester grade point average (GPA)? First-year college students from three independent universities provided sleep actigraphy for a month early in their spring academic term across five studies.\n\n\n\n\n\n\nJul 25, 2023\n\n\nShunyi Zheng\n\n\n\n\n\n\n\n\nRTG tinned fish\n\n\n\n\n\nThe Rainbow Tomatoes Garden sells hundreds of kinds of canned seafood, and produces a complete table including 27 different attributes, from Latin name to sodium content. Write code to clean the data, then analyze factors affecting price and composition.\n\n\n\n\n\n\nAug 8, 2023\n\n\nShunyi (Jess) Zheng\n\n\n\n\n\n\n\n\nRail trails and property values\n\n\n\n\n\nRail trails are a great recreation opportunity, but do homes near them rise in value? A retrospective observational study of house values.\n\n\n\n\n\n\nJul 27, 2022\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nReligion and analytic thinking\n\n\n\n\n\nA controversial study found that encouraging analytic thinking also reduced religious belief. A replication attempt collected much more data to try to confirm the hypothesis; is it supported by the data? A simple randomized experiment with continuous outcome and demographic controls.\n\n\n\n\n\n\nFeb 25, 2017\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nScience Forums\n\n\n\n\n\nA random sample of discussions at a large science discussion forum, with metadata about each.\n\n\n\n\n\n\nFeb 22, 2017\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nServing sizes and plate sizes\n\n\n\n\n\nDo people eat more food if their meals are served on larger plates? A randomized experiment with demographic and psychological control variables.\n\n\n\n\n\n\nSep 1, 2019\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nSpider nuptial gifts and fraud\n\n\n\n\n\nMale spiders give “gifts” of silk-wrapped food to female spiders to convince them to mate. Sometimes, they may cheat by gifting something other than food. Under what conditions do they cheat, and how much silk do they use to wrap fake gifts? Analyze an experiment on spiders in the lab.\n\n\n\n\n\n\nJun 15, 2017\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nSugary drinks and portion limits\n\n\n\n\n\nPublic health officials are interested in policies that would reduce consumption of sugary drinks. What policies work and what policies backfire? A factorial experiment.\n\n\n\n\n\n\nJun 14, 2017\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nTestosterone and diversity\n\n\n\n\n\nWhen students work on group projects, they work better when their groups are diverse. This study asked if that depends on the testosterone level of the group, and if high-testosterone groups benefit less from diversity.\n\n\n\n\n\n\nJun 18, 2018\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nUS College Scorecard\n\n\n\n\n\nAn extensive survey of American colleges and universities, recording the demographics of their graduates, typical salaries, and much more.\n\n\n\n\n\n\nAug 8, 2023\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nWorld Development Indicators\n\n\n\n\n\nThe World Bank’s World Development Indicators (WDI) compile development information about countries around the world. Using ten years of data, study development, political stability, pollution, and other factors at the national and regional levels.\n\n\n\n\n\n\nJun 20, 2023\n\n\nShunyi Zheng\n\n\n\n\n\n\nNo matching items"
+    "text": "All datasets are listed below, and can be filtered by statistical method on the right. A downloadable spreadsheet of all datasets is also available for more advanced searches.\n\n\n\n\n\n\n\n\n\n\nAnAge Longevity Database\n\n\n\n\n\nThe AnAge database includes information on the longevity of thousands of animal species, allowing studies of the factors related to long lifespans.\n\n\n\n\n\n\nDec 27, 2021\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nBird genetic diversity\n\n\n\n\n\nEvolutionary theory suggests that species spread over a wider geographic range should have greater genetic diversity. Does this hold true in birds? An observational study of 387 bird species.\n\n\n\n\n\n\nJul 10, 2019\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nBloodletting\n\n\n\n\n\nBloodletting—deliberately withdrawing large amounts of blood from patients—was a common medical practice for centuries, until evidence was finally collected on its efficacy. Use an early observational dataset to explore its use for pneumonia.\n\n\n\n\n\n\nJun 8, 2023\n\n\nGordon Weinberg\n\n\n\n\n\n\n\n\nCore temperature during surgery\n\n\n\n\n\nIs low core temperature during surgery associated with poor surgical outcomes or death? Use logistic and survival analysis to study an observational dataset.\n\n\n\n\n\n\nMay 17, 2023\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nDry beans\n\n\n\n\n\nSort and classify beans using features extracted from images.\n\n\n\n\n\n\nDec 27, 2021\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nEuropean protected ham\n\n\n\n\n\nIn Europe, some types of ham—like Black Forest ham—can only be legally produced in specific geographic regions. Does the size of those regions affect the price of the ham?\n\n\n\n\n\n\nJun 9, 2023\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nGalaxy mass prediction\n\n\n\n\n\nGalaxies span a range of masses. Can students learn a statistical model that associates a galaxy’s mass with its brightness as measured at six wavelengths along with an estimate of its distance from the Earth?\n\n\n\n\n\n\nFeb 3, 2023\n\n\nPeter Freeman\n\n\n\n\n\n\n\n\nHealth exams in Vietnam\n\n\n\n\n\nAs access to health care increases, public health advocates encourage ordinary people to get regular check-ups to detect problems before they become serious (and expensive). But not everyone follows this advice. This survey of people in Vietnam explores the factors leading people to get regular check-ups.\n\n\n\n\n\n\nDec 8, 2022\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nHouse prices in Ames, Iowa\n\n\n\n\n\nRecords of homes sold in Ames, Iowa, and their sale prices, including detailed covariates that could be used to predict home prices.\n\n\n\n\n\n\nAug 14, 2019\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nInsect attraction to light\n\n\n\n\n\nWhat kinds of lights are insects most attracted to? Analyze the results of a factorial experiment using light traps.\n\n\n\n\n\n\nDec 8, 2022\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nMakeup and male attractiveness\n\n\n\n\n\nMakeup is often used by women, but does makeup make men more attractive? This study had participants rate photos of men with and without makeup on.\n\n\n\n\n\n\nNov 4, 2022\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nMapping Police Violence\n\n\n\n\n\nEach year in the United States, around 1,000 people are killed by police. Some of these deaths are accidental, in traffic accidents and other incidents; some are deliberate killings considered legally justified by authorities; and others are considered unjustified or even lead to the prosecution of the police officer. Explore data on over 10,000 such killings to identify patterns in the people killed, the stated reasons for their killings, and the situations leading to their deaths.\n\n\n\n\n\n\nSep 6, 2023\n\n\nJessica Zhiyu Guo\n\n\n\n\n\n\n\n\nNightly sleep time and GPA in first-years\n\n\n\n\n\nDoes nightly sleep duration predict change in end-of-semester grade point average (GPA)? First-year college students from three independent universities provided sleep actigraphy for a month early in their spring academic term across five studies.\n\n\n\n\n\n\nJul 25, 2023\n\n\nShunyi Zheng\n\n\n\n\n\n\n\n\nRTG tinned fish\n\n\n\n\n\nThe Rainbow Tomatoes Garden sells hundreds of kinds of canned seafood, and produces a complete table including 27 different attributes, from Latin name to sodium content. Write code to clean the data, then analyze factors affecting price and composition.\n\n\n\n\n\n\nAug 8, 2023\n\n\nShunyi (Jess) Zheng\n\n\n\n\n\n\n\n\nRail trails and property values\n\n\n\n\n\nRail trails are a great recreation opportunity, but do homes near them rise in value? A retrospective observational study of house values.\n\n\n\n\n\n\nJul 27, 2022\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nReligion and analytic thinking\n\n\n\n\n\nA controversial study found that encouraging analytic thinking also reduced religious belief. A replication attempt collected much more data to try to confirm the hypothesis; is it supported by the data? A simple randomized experiment with continuous outcome and demographic controls.\n\n\n\n\n\n\nFeb 25, 2017\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nScience Forums\n\n\n\n\n\nA random sample of discussions at a large science discussion forum, with metadata about each.\n\n\n\n\n\n\nFeb 22, 2017\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nServing sizes and plate sizes\n\n\n\n\n\nDo people eat more food if their meals are served on larger plates? A randomized experiment with demographic and psychological control variables.\n\n\n\n\n\n\nSep 1, 2019\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nSpider nuptial gifts and fraud\n\n\n\n\n\nMale spiders give “gifts” of silk-wrapped food to female spiders to convince them to mate. Sometimes, they may cheat by gifting something other than food. Under what conditions do they cheat, and how much silk do they use to wrap fake gifts? Analyze an experiment on spiders in the lab.\n\n\n\n\n\n\nJun 15, 2017\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nSugary drinks and portion limits\n\n\n\n\n\nPublic health officials are interested in policies that would reduce consumption of sugary drinks. What policies work and what policies backfire? A factorial experiment.\n\n\n\n\n\n\nJun 14, 2017\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nTestosterone and diversity\n\n\n\n\n\nWhen students work on group projects, they work better when their groups are diverse. This study asked if that depends on the testosterone level of the group, and if high-testosterone groups benefit less from diversity.\n\n\n\n\n\n\nJun 18, 2018\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nTraffic stops in Connecticut\n\n\n\n\n\nRecords for every traffic stop made by the Connecticut State Police over several years, including the reason for the stop and demographic details. Use classifiers to study whether stop and search decisions show signs of bias.\n\n\n\n\n\n\nFeb 9, 2019\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nUS College Scorecard\n\n\n\n\n\nAn extensive survey of American colleges and universities, recording the demographics of their graduates, typical salaries, and much more.\n\n\n\n\n\n\nAug 8, 2023\n\n\nAlex Reinhart\n\n\n\n\n\n\n\n\nWorld Development Indicators\n\n\n\n\n\nThe World Bank’s World Development Indicators (WDI) compile development information about countries around the world. Using ten years of data, study development, political stability, pollution, and other factors at the national and regional levels.\n\n\n\n\n\n\nJun 20, 2023\n\n\nShunyi Zheng\n\n\n\n\n\n\nNo matching items"
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     "text": "References\nD De Cock (2011). Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project. Journal of Statistics Education 19 (3). https://doi.org/10.1080/10691898.2011.11889627"
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-    "text": "The World Bank compiles a database of World Development Indicators, which compile data from various sources intended to give the most current and accurate data about countries worldwide. Over a thousand different variables are available, updated yearly.\nThis dataset compiles a selection of 40 interesting variables for 266 countries and regions across 10 years, from 2013 to 2022."
+    "text": "In the United States, police officers routinely make traffic stops for a variety of reasons, like speeding, reckless driving, or use of cell phones while driving. They’re one of the main ways ordinary people interact with police, and while most traffic stops are completely routine, others are more serious, leading to searches, arrests, and sometimes violence.\nBecause traffic stops are so common, they’re also widely scrutinized. Some allege that officers are racially biased in who they stop, which vehicles they choose to search, or who they decide to give tickets instead of warnings to. Until recent years, detailed data was not available to help answer these allegations, but new initiatives have begun to collect extensive data from police departments across many states."
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-    "text": "The World Bank compiles a database of World Development Indicators, which compile data from various sources intended to give the most current and accurate data about countries worldwide. Over a thousand different variables are available, updated yearly.\nThis dataset compiles a selection of 40 interesting variables for 266 countries and regions across 10 years, from 2013 to 2022."
+    "text": "In the United States, police officers routinely make traffic stops for a variety of reasons, like speeding, reckless driving, or use of cell phones while driving. They’re one of the main ways ordinary people interact with police, and while most traffic stops are completely routine, others are more serious, leading to searches, arrests, and sometimes violence.\nBecause traffic stops are so common, they’re also widely scrutinized. Some allege that officers are racially biased in who they stop, which vehicles they choose to search, or who they decide to give tickets instead of warnings to. Until recent years, detailed data was not available to help answer these allegations, but new initiatives have begun to collect extensive data from police departments across many states."
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     "section": "Data",
-    "text": "Data\nEach row represents one country, territory, or region in one year.\nThe World Bank includes some geographic groupings, such as “Sub-Saharan Africa” and “Pacific island small states”, as well as individual countries.\nNot all variables are available for all countries in all years, and more recent data is missing more often than older data.\n\nData preview\n\n world-bank.csv \n\n\n\n\n\n\n\nVariable descriptions\nThese descriptions are adapted directly from the World Bank’s data documentation. Codes in parentheses (such as SE.COM.DURS) are the World Bank codes for these variables. Search for these codes in the data series listed in the DataBank to find detailed descriptions of the sources and definitions for each variable.\n\n\n\nVariable\nDescription\n\n\n\n\nCountry Name\nName of country or region\n\n\nCountry Code\nUnique three-letter code identifying the country\n\n\nRegion\nGeographic region the country is in\n\n\nIsCountry\n1 if this row represents a country, 0 if it is an aggregated region (like Sub-Saharan Africa or all high-income countries)\n\n\nIncome group\nCountry’s income group as of 2023 (high-income, upper-middle-income, lower-middle-income, or low-income), based on its gross national income (GNI), as defined by the World Bank\n\n\nYear\nYear the values were observed in\n\n\nAlcohol\nTotal alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age) (SH.ALC.PCAP.LI)\n\n\nBattleDeaths\nBattle-related deaths (number of people) (VC.BTL.DETH)\n\n\nBirth\nBirth rate, crude (per 1,000 people) (SP.DYN.CBRT.IN)\n\n\nBirthSex\nSex ratio at birth (male births per female births) (SP.POP.BRTH.MF)\n\n\nCO2Emissions\nCO2 emissions (metric tons per capita) (EN.ATM.CO2E.PC)\n\n\nCompulsoryEducation\nCompulsory education, duration (years) (SE.COM.DURS)\n\n\nDeath\nDeath rate, crude (per 1,000 people) (SP.DYN.CDRT.IN)\n\n\nDeathsCD\nDeaths caused by communicable diseases and maternal, prenatal and nutrition conditions (% of total deaths) (SH.DTH.COMM.ZS)\n\n\nDeathsNCD\nDeaths caused by non-communicable diseases (% of total) (SH.DTH.NCOM.ZS)\n\n\nDensity\nPopulation density (people per sq. km of land area) (EN.POP.DNST)\n\n\nDiabetes\nDiabetes prevalence (% of population ages 20 to 79) (SH.STA.DIAB.ZS)\n\n\nElectricity\nAccess to electricity (% of population) (EG.ELC.ACCS.ZS)\n\n\nFertility\nFertility rate, total (births per woman) (SP.DYN.TFRT.IN)\n\n\nFixedTelephone\nFixed telephone subscriptions (IT.MLT.MAIN)\n\n\nForestArea\nForest area (% of land area) (AG.LND.FRST.ZS)\n\n\nGDP\nGross domestic product (current US$) (NY.GDP.MKTP.CD)\n\n\nGenderEducation\nGender parity index (GPI) for tertiary education enrollment: ratio of women to men enrolled (SE.ENR.TERT.FM.ZS)\n\n\nGenderEquality\nCPIA gender equality rating, measuring whether the government has programs and institutions to promote gender equality in education, health, the economy, and the law (1=low to 6=high) (IQ.CPA.GNDR.XQ)\n\n\nGovernmentExpenditure\nGeneral government final consumption expenditure (constant 2015 US$) (NE.CON.GOVT.KD)\n\n\nHomicide\nIntentional homicides (per 100,000 people) (VC.IHR.PSRC.P5)\n\n\nIncome\nAdjusted net national income (constant 2015 US$) (NY.ADJ.NNTY.KD)\n\n\nInternet\nIndividuals using the Internet (% of population) (IT.NET.USER.ZS)\n\n\nLandArea\nLand area (sq. km) (AG.LND.TOTL.K2)\n\n\nLegalRights\nStrength of legal rights index (0=weak to 12=strong) (IC.LGL.CRED.XQ)\n\n\nLiteracy\nLiteracy rate, adult total (% of people ages 15 and above) (SE.ADT.LITR.ZS)\n\n\nMilitary\nMilitary expenditure (% of general government expenditure) (MS.MIL.XPND.ZS)\n\n\nMobile\nMobile cellular subscriptions (IT.CEL.SETS)\n\n\nPM2.5\nPM2.5 air pollution, mean annual exposure (micrograms per cubic meter) (EN.ATM.PM25.MC.M3)\n\n\nPlaneDepartures\nNumber of takeoffs of planes operated by air carriers registered in this country, domestically or from foreign airports (IS.AIR.DPRT)\n\n\nPlanePassengers\nNumber of passengers carried by air carriers registered in this country (IS.AIR.PSGR)\n\n\nPoliticalStability\nPolitical stability and absence of violence/terrorism score (normalized as a z score) (PV.EST)\n\n\nPopulation\nPopulation, total (SP.POP.TOTL)\n\n\nPoverty\nPercentage of population who are multidimensionally poor (% of total population) (SI.POV.MDIM)\n\n\nRural\nRural population (% of total population) (SP.RUR.TOTL.ZS)\n\n\nRuralArea\nRural land area (sq. km) (AG.LND.TOTL.RU.K2)\n\n\nSuicide\nSuicide mortality rate (per 100,000 population) (SH.STA.SUIC.P5)\n\n\nTaxRevenue\nTax revenue (% of GDP) (GC.TAX.TOTL.GD.ZS)\n\n\nUnemployment\nUnemployment, total (% of total labor force) (modeled ILO estimate) (SL.UEM.TOTL.ZS)\n\n\nUrban\nUrban population (% of total population) (SP.URB.TOTL.IN.ZS)\n\n\nUrbanArea\nUrban land area (sq. km) (AG.LND.TOTL.UR.K2)"
+    "text": "Data\nThis dataset records all traffic stops made by the Connecticut State Police (not the police departments of individual towns and cities) from October 2013 to March 2015, amounting to nearly 320,000 traffic stops. Each row is one stop.\nThe data summarizes each stop, including driver demographics, the reason for the stop, and whether a search was conducted or contraband was found.\n\nData preview\n\n connecticut-stops.csv.gz \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nid\nA unique ID assigned to each stop.\n\n\nstate\nThe state in which each stop occurred (always CT, for Connecticut, in this data)\n\n\nstop_date\nDate of the traffic stop (YYYY-MM-DD)\n\n\nstop_time\nTime the stop occurred (24-hour time, HH:MM)\n\n\nlocation_raw\nLocation name from original records, used to identify the county\n\n\ncounty_name\nCounty in which the stop occurred\n\n\ncounty_fips\nThe unique FIPS ID code for the county\n\n\nfine_grained_location\nA more detailed location, not in a standardized format\n\n\npolice_department\nName of the police department making the stop\n\n\ndriver_gender\nGender of the driver, as recorded by the officer making the stop\n\n\ndriver_age_raw\nDriver’s age from the original dataset. Not always in a consistent format\n\n\ndriver_age\nDriver’s age; NA if under 15 or greater than 99\n\n\ndriver_race_raw\nThe driver’s race, as recorded by the officer. Not in a standard format.\n\n\ndriver_race\nThe driver’s race, standardized to these options: White, Black, Hispanic, Asian, Other, and NA (unknown). Asian includes Asian, Pacific Islander, and Indian. Native Americans are listed as Other, and anyone with Hispanic ethnicity is listed as Hispanic.\n\n\nviolation_raw\nThe violation for which the driver was stopped, from the original data\n\n\nviolation\nThe violation broken into standard categories, like Speeding, Lights, or Moving violation. Some stops involve multiple violations, separated by commas.\n\n\nsearch_conducted\nTRUE if the officer searched the vehicle; FALSE otherwise.\n\n\nsearch_type_raw\nJustification for the search, if conducted, as written by the police agency\n\n\nsearch_type\nJustification for the search broken down into consistent categories like Consent (driver gave consent). Many are listed as Other with no reason given\n\n\ncontraband_found\nWhether the search found contraband (like drugs). TRUE or FALSE, and FALSE if no search was performed.\n\n\nstop_outcome\nThe result of the stop: arrest, ticket, warning, and so on.\n\n\nis_arrested\nWhether an arrest was made (TRUE or FALSE).\n\n\nofficer_id\nA unique ID for the officer making the stop.\n\n\nstop_duration\nDuration of the stop, broken into categories (1-5 min, 16-30 min, and so on)."
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     "section": "Questions",
-    "text": "Questions\nMany questions can be explored with this dataset. The questions below are intended merely as a starting point; instructors and students can undoubtedly find many other interesting relationships to explore.\n\nExplore the differences between income groups. For example, does the percentage of people using the Internet vary between income groups? Use graphical EDA or an ANOVA to study the differences, picking one year to focus on.\n(Fertility, alcohol consumption, forest area, and other variables may also be interesting to compare by income group.)\nExplore the relationship between Internet access and political stability for all countries in 2016. Is there a positive or negative relationship? Fit a regression model and interpret the coefficients.\nNow control for the country’s income group. Test whether an interaction term is necessary. Interpret the results. What does this interaction imply? Use a plot of the relationships to illustrate your results.\nMany other relationships between variables could be explored in EDA, regression, or ANOVA. For instance:\n\nCarbon dioxide emissions and PM 2.5 exposure, and how this relationship depends on GDP or income group\nGender parity in education (GenderEducation) and gender equality promotion by government policy (GenderEquality)\nThe strength of legal rights (LegalRights) and political instability and violence (PoliticalStability)\nDeaths in battle (BattleDeaths) and political stability (PoliticalStability)\nYears of compulsory education (CompulsoryEducation) and literacy rate (Literacy)\n\nOther variables may change over time in interesting ways, such as airplane travel (PlaneDepartures and PlanePassengers), forest area (ForestArea), and diabetes prevalence (Diabetes).\nFor any research question involving hypothesis tests, consider: What population can this data be said to be drawn from? Are tests necessary to generalize to that population?"
+    "text": "Questions\n\nConduct an exploratory analysis of the data. What stop types are most common? Is there variation by race or gender? What types of stop are most likely to lead to a ticket?\nOne way to look for racial discrimination in the decision to search a vehicle is called the “outcome test”. If officers do not discriminate, and instead use only objective evidence to decide a car should be searched for drugs, then the rate at which they find drugs should be the same across racial groups. But if officers unfairly target a racial group for searches, they may conduct more searches of drivers in that group without finding any drugs.\nBuild a model to predict whether contraband is found after a search is conducted, using race and other appropriate covariates. Does the outcome test appear to show discrimination? Carefully explain the caveats of your results, discussing confounding variables and mentioning other types of data you would like to have.\nIs there any evidence that traffic stops are most common certain times of year or on certain days of the week? Before conducting your analysis, make some hypotheses about what you expect to find. (Do you think officers would be less likely to make traffic stops in the cold winter months, for example?)"
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-    "text": "References\nThe World Development Indicators were downloaded from the DataBank service at https://databank.worldbank.org/source/world-development-indicators. All values are for the years 2013-2022. World Bank data is provided under a Creative Commons Attribution 4.0 International License."
+    "text": "References\nData collected and cleaned by the Stanford Open Policing Project, released under the Open Data Commons Attribution License. Code and data descriptions available on GitHub. The Open Policing Project has released similar data files for many other states.\nFor a rigorous analysis of this data, see E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, D. Jenson, A. Shoemaker, V. Ramachandran, P. Barghouty, C. Phillips, R. Shroff, and S. Goel. (2020) “A large-scale analysis of racial disparities in police stops across the United States”. Nature Human Behavior 4, 736-745. https://doi.org/10.1038/s41562-020-0858-1"
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-    "text": "In the spider species Pisaura mirabilis, male spiders woo female spiders to mate by giving them gifts: a piece of food (like a freshly captured insect) wrapped in spider silk. If the gift is good, the female spider allows the male to mate.\nIt turns out that sometimes male spiders cheat. Instead of wrapping food in silk, they wrap something else, such as bits of a plant or an empty insect exoskeleton they’ve already eaten from. This saves effort, though it runs the risk of the female finding out and not accepting the gift; to avoid this, males wrap the gifts in many extra layers of silk, so they can mate before the female fully unwraps the gift and discovers what’s inside.\nResearchers wanted to know why male spiders would cheat. Do they only cheat if they are already starving and can’t spare the food for a gift, or do they cheat opportunistically regardless of how much food they have to spare?\nIn the experiment presented here, male spiders kept in a laboratory were fed different diets, and attributes of the spider and his gift were recorded to see if there was a connection between the spider’s food and condition and the type of gift he produced."
+    "text": "Race and policing are some of the most inescapable topics in today’s America. There are large political arguments over the proper role of police, the effect of their tactics on historically disadvantaged groups, and their perceived biases. These controversies are amplified by a number of police killings of unarmed people, particularly African Americans, which have led to massive protests over the excessive use of force and sometimes to the prosecution of the officers involved.\nThere is no central government database recording all police uses of force, or even all killings by police officers. Several journalistic and advocacy groups have attempted to fill the gaps by compiling government data and news reporting to build large datasets including police killings and the circumstances surrounding them.\nThis dataset, Mapping Police Violence, is compiled by the advocacy group Campaign Zero. It builds on other databases, such as one compiled by the Washington Post, to include information from news reporting and other sources to provide additional context about each death. It is the most comprehensive dataset detailing all kinds of police violence incidents and helps us understand the scale of police violence and the differences across race/ethnicity and different geographical locations.\nWe can also combine this with the 2020 Census data on the county level to understand the prevalence of police violence and racial/ethnic differences in prevalence in different counties. The methodology is last updated on Oct 3, 2022, and the dataset is downloaded on Aug 7, 2023."
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-    "text": "In the spider species Pisaura mirabilis, male spiders woo female spiders to mate by giving them gifts: a piece of food (like a freshly captured insect) wrapped in spider silk. If the gift is good, the female spider allows the male to mate.\nIt turns out that sometimes male spiders cheat. Instead of wrapping food in silk, they wrap something else, such as bits of a plant or an empty insect exoskeleton they’ve already eaten from. This saves effort, though it runs the risk of the female finding out and not accepting the gift; to avoid this, males wrap the gifts in many extra layers of silk, so they can mate before the female fully unwraps the gift and discovers what’s inside.\nResearchers wanted to know why male spiders would cheat. Do they only cheat if they are already starving and can’t spare the food for a gift, or do they cheat opportunistically regardless of how much food they have to spare?\nIn the experiment presented here, male spiders kept in a laboratory were fed different diets, and attributes of the spider and his gift were recorded to see if there was a connection between the spider’s food and condition and the type of gift he produced."
+    "text": "Race and policing are some of the most inescapable topics in today’s America. There are large political arguments over the proper role of police, the effect of their tactics on historically disadvantaged groups, and their perceived biases. These controversies are amplified by a number of police killings of unarmed people, particularly African Americans, which have led to massive protests over the excessive use of force and sometimes to the prosecution of the officers involved.\nThere is no central government database recording all police uses of force, or even all killings by police officers. Several journalistic and advocacy groups have attempted to fill the gaps by compiling government data and news reporting to build large datasets including police killings and the circumstances surrounding them.\nThis dataset, Mapping Police Violence, is compiled by the advocacy group Campaign Zero. It builds on other databases, such as one compiled by the Washington Post, to include information from news reporting and other sources to provide additional context about each death. It is the most comprehensive dataset detailing all kinds of police violence incidents and helps us understand the scale of police violence and the differences across race/ethnicity and different geographical locations.\nWe can also combine this with the 2020 Census data on the county level to understand the prevalence of police violence and racial/ethnic differences in prevalence in different counties. The methodology is last updated on Oct 3, 2022, and the dataset is downloaded on Aug 7, 2023."
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-    "text": "Data\nIn each experiment, male spiders of roughly the same age were placed in small boxes which previously held sexually receptive female spiders, to make them think they should prepare a gift. The experimenters provided the males with a live cricket nymph nearby, which they could presumably use as a gift; if the male didn’t catch it within twenty minutes, or caught it but didn’t make a gift with it, the experiment ended and was tried again a day later.\nThe weight of the prey before and after it was made into a gift was measured, to see if the male ate some of it before wrapping it into a gift. The weight of the silk used to wrap the prey was also recorded. (In a few cases, the prey and silk couldn’t be separated, and weights are not recorded. You’ll have to exclude these cases.)\n\nData preview\n\n spider-lab-gifts.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nMALE ID\nUnique ID for each male spider\n\n\nFOOD TREATMENT\nThe diet the male was fed. HF: high food (two insects per week), LF: low food (one insect per week)\n\n\nN TRIAL\nThe number of the trial (e.g. 2 if it took two tries for the male to make a gift)\n\n\nPREY WEIGHT BEFORE TRIAL (mg)\nThe weight of the cricket nymph before the trial (milligrams)\n\n\nGift weight (mg)\nWeight of the gift produced (milligrams)\n\n\nSILK WEIGHT (mg)\nWeight of the silk used to wrap the gift (milligrams)\n\n\nPREY WEIGHT AFTER (gift - silk weight)\nWeight of the prey after it was used to make a gift (milligrams)\n\n\nPREY WEIGHT LOSS\nWeight lost by prey (e.g. by the male spider eating some of it before making it into a gift) (milligrams)\n\n\nMALE MASS BEFORE TRIAL (mg)\nMass of the male spider (milligrams)\n\n\nMALE CEPH WIDTH (mm)\nWidth of the male spider’s cephalothorax (millimeters)\n\n\nMALE R BODY INDEX\nSee Questions, below\n\n\nLATENCY TO WRAP (sec)\nTime elapsed between capturing the prey and starting to make a gift out of it (seconds)\n\n\nMALE MASS AFTER TRIAL (mg)\nMass of the male spider after making the gift (milligrams)\n\n\nMALE WEIGHT difference (bef-after)\nDifference between male weight (before - after)\n\n\nTOT WRAPPING DURATION (sec)\nTotal time the male spent wrapping the gift (seconds)"
+    "text": "Data\nEach row represents one death in an incident of police violence. There are 11,861 deaths recorded from 2012 to 2023.\nMapping Police Violence include detailed descriptions of each incident, including date and victim’s age, race, gender, and an image. It also includes more details about the incident including the location to the street address, the cause of death (can be multiple), circumstances, agency responsible, initial reasons, officer names and history, and so on.\nThe Census data provides 2020 demographic information for each county. It can be joined with the police violence data using the FIPS column, which uniquely identifies counties, though some incidents of violence do not have a county recorded or the county could not be matched to a FIPS code.\nNot all variables are available for all incidents. For older incidents, some data around prosecution (and its result) for the officer could be available.\n\nData preview\n\n mapping-police-violence.csv.gz \n\n\n\n\n\n\n county-census-2020.csv.gz \n\n\n\n\n\n\n\nVariable Descriptions\nThe dataset codebook from Mapping Police Violence and the U.S. Census Bureau documentation contain the full list of variables. This is an extract of a few useful variables from both tables. Note that many variables can have multiple values, separated by commas, in the Mapping Police Violence dataset.\n\nMapping Police Violence\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nname\nName of victim killed by law enforcement\n\n\nage\nAge of victim at time of death\n\n\ngender\nVictim’s indicated gender by news/official reports\n\n\nrace\nRace of victim according to news/official reports\n\n\ndate\nDate of lethal action on victim, in MM/DD/YYYY format\n\n\ncity\nCity where incident took place\n\n\nstate\nState where incident took place\n\n\ncounty\nCounty where incident took place\n\n\nFIPS\nFIPS code identifying the county where the incident took place. Not available when county is not given, if county is spelled incorrectly, or in most incidents in Connecticut and Alaska, which have unusual county systems\n\n\nagency_responsible\nAgency of law enforcement officer who took lethal action and responsible for killing\n\n\ncause_of_death\nCause of death of the victim, such as gunshot, Taser, vehicle, or drowning. Multiple reasons can be separated by commas\n\n\nsigns_of_mental_illness\nIndicates whether official/news reports mentioned any signs of mental illness or mental health crises: Yes/No/Unknown/Drug or Alcohol Use\n\n\nallegedly_armed\nIndicates whether victim was armed according to news/official sources: Allegedly Armed/Unarmed/Unclear/Vehicle\n\n\noff_duty_killing\nIndicates whether officer was working in an official capacity at the time lethal action was taken\n\n\nencounter_type\nInitial reported reason(s) for police to be on the scene prior to using deadly force\n\n\nofficer_names\nName of officer responsible for highest level of force\n\n\nofficer_races\nRace of officer responsible for highest level of force\n\n\nofficer_known_past_shootings\nIndicates if officer has had a previous incident of police violence in the database\n\n\n\n\n\nCensus Annual County Resident Population Estimates by Age, Sex, Race, and Hispanic Origin\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nSTNAME\nState Name\n\n\nCTYNAME\nCounty Name\n\n\nFIPS\nFIPS code identifying the county\n\n\nTOT_POP\nTotal population\n\n\nNHWA_MALE/NHWA_FEMALE\nNot Hispanic, White alone male/female population\n\n\nNHBA_MALE/NHBA_FEMALE\nNot Hispanic, Black or African American alone male/female population\n\n\nNHIA_MALE/NHIA_FEMALE\nNot Hispanic, American Indian and Alaska Native alone male/female population\n\n\nNHAA_MALE/NHAA_FEMALE\nNot Hispanic, Asian alone male/female population\n\n\nNHNA_MALE/NHNA_FEMALE\nNot Hispanic, Native Hawaiian and Other Pacific Islander alone male/female population\n\n\nNHTOM_MALE/NHTOM_FEMALE\nNot Hispanic, Two or More Races male/female population\n\n\nH_MALE/H_FEMALE\nHispanic male/female population"
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-    "text": "Questions\n\nDoes the male body weight (before the experiment) differ significantly between males fed the two different diets? What about their cephalothorax width?\nIf the prey lost weight, the male cheated and ate some of the prey before making a gift. Is the weight loss of the prey positively related to the mass of silk used to wrap it into a gift, as we’d expect if the male was hiding an inferior gift? Produce supporting graphs. Does the relationship depend on whether the male was fed the high or low food diet?\n(In three cases, the prey gained weight. According to the researchers, this is “most likely due to spiders’ injection of digestive fluids that serve for liquefying the prey during feeding”.)\nDoes the latency before wrapping the fit correlate with the weight loss of the gift, suggesting males waited longer to wrap inferior gifts (or waited too long and got tempted by the juicy cricket)?\nIs there a difference in the weight loss of the prey between males fed the high food and low food diets? What about in the amount of silk used?\nRegress the total time spent wrapping the gift against the mass of the gift. How many milligrams of silk can the male wrap per second?\nAccording to the researchers, the MALE R BODY INDEX variable was calculated as “the residuals of a regression of body mass on body size (cephalothorax width)”. Perform the regression yourself and try to match your results against the researchers. Does it match?"
+    "text": "Questions\n\nDo some EDA. What’s the age and gender distribution of victims? What are the most common encounter types in the dataset? What are the most common causes of death? Explore these questions, and others that seem interesting in the data, with charts and graphs.\nOne main question we would like to explore is that whether the demographic composition of the locality affects the occurrences of police violence. We can use the Census data to provide information on 7 groups (non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, non-Hispanic American Indian/Alaskan Native, non-Hispanic Native Hawaiian or other Pacific Islanders, non-Hispanic two or more races, and Hispanic) in each county. Build a model predicting the count of police violence in each county in the dataset based on their racial/ethnic minority percentage and other factors you think might be relevant. What does the result suggest?\nAnother argument people often make is that officers responsible for police violence against minorities often bear no consequences for their actions. Based on this data, build a logistic model to predict the likelihood of the police officer(s) responsible being charged with all factors you think might be relevant. What does the result suggest?\nSome people suggest that signs of mental health issues and/or drug use might also affect how police respond to a person, say we categorize any mentioning of a gunshot in the cause of death as an indicator for a higher level of force used. Does the data seem to support this claim?\nSimilarly, does the perception of the individual being armed appear to influence the extent of force employed by the police?"
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-    "text": "References\nGhislandi PG, Beyer M, Velado P, Tuni C (2017). Silk wrapping of nuptial gifts aids cheating behaviour in male spiders. Behavioral Ecology 28 (3), 744-749. https://doi.org/10.1093/beheco/arx028\nGhislandi P, Beyer M, Velado P, Tuni C (2017). Data from: Silk wrapping of nuptial gifts aids cheating behaviour in male spiders. Dryad Digital Repository. https://doi.org/10.5061/dryad.kb1fh"
+    "text": "References\nPolice violence data aggregated by Mapping Police Violence. Data retrieved on August 7, 2023.\nCensus data from the US Census Bureau’s County Population by Characteristics data. Technical documentation."
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-    "text": "The common bean (Phaseolus vulgaris) is a plant widely cultivated for its dry seeds—beans. The species has long been cultivated for food and has developed into many varieties; many common beans, such as kidney beans, pinto beans, navy beans, and wax beans, are all varieties of the common bean. Because there are many varieties within the species, there are many varieties of seed that farmers can choose to plant, and each variety is suited for different growing climates and for different markets.\nHowever, in countries like Turkey, most farmers do not use commercially produced seeds certified to be all of one specific variety. They use seeds they obtain from their own crops or from other farmers, which may contain many different varieties of bean. To sort out specific varieties would require a trained expert to carefully sort the beans—which would be quite difficult when planting a field takes many thousands of seeds!\nAs a result, there is some demand for systems that can automatically classify seeds by variety, for example by using images of the seeds. If seeds can be accurately sorted by computer, a machine could automatically sort seeds at high speed, allowing farmers to sort their crops, pick the best varieties for their fields and their markets, and sell their seeds to other farmers who want a specific variety.\nTo collect this data, researchers used a camera to take photographs of 13,611 beans of seven different varieties. (This study was conducted in Turkey, so the varieties are known by their Turkish names: Barbunya, Battal, Bombay, Calı, Dermason, Horoz, Tombul, Selanik and Seker.) The beans were of known variety. The researchers then used image processing code to extract features from the images, including the bean sizes and several measures of shape. The goal was to use these features to classify the variety of the beans; if this is possible, one could build a system using a camera and computer to automatically classify beans by variety."
+    "text": "The AnAge Database of Animal Ageing and Longevity is a “curated database of ageing and life history in animals” that was “primarily developed for comparative biology studies.” It contains information, compiled from hundreds of scientific papers, for over 4,200 species. It’s mainly meant for studies of aging, so for each species, it includes variables such as lifespan, age of sexual maturity, adult body mass, typical body temperature, and metabolic rate. We can use this data to learn more about the relationships between each variable and the key outcome variable: lifespan."
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-    "text": "The common bean (Phaseolus vulgaris) is a plant widely cultivated for its dry seeds—beans. The species has long been cultivated for food and has developed into many varieties; many common beans, such as kidney beans, pinto beans, navy beans, and wax beans, are all varieties of the common bean. Because there are many varieties within the species, there are many varieties of seed that farmers can choose to plant, and each variety is suited for different growing climates and for different markets.\nHowever, in countries like Turkey, most farmers do not use commercially produced seeds certified to be all of one specific variety. They use seeds they obtain from their own crops or from other farmers, which may contain many different varieties of bean. To sort out specific varieties would require a trained expert to carefully sort the beans—which would be quite difficult when planting a field takes many thousands of seeds!\nAs a result, there is some demand for systems that can automatically classify seeds by variety, for example by using images of the seeds. If seeds can be accurately sorted by computer, a machine could automatically sort seeds at high speed, allowing farmers to sort their crops, pick the best varieties for their fields and their markets, and sell their seeds to other farmers who want a specific variety.\nTo collect this data, researchers used a camera to take photographs of 13,611 beans of seven different varieties. (This study was conducted in Turkey, so the varieties are known by their Turkish names: Barbunya, Battal, Bombay, Calı, Dermason, Horoz, Tombul, Selanik and Seker.) The beans were of known variety. The researchers then used image processing code to extract features from the images, including the bean sizes and several measures of shape. The goal was to use these features to classify the variety of the beans; if this is possible, one could build a system using a camera and computer to automatically classify beans by variety."
+    "text": "The AnAge Database of Animal Ageing and Longevity is a “curated database of ageing and life history in animals” that was “primarily developed for comparative biology studies.” It contains information, compiled from hundreds of scientific papers, for over 4,200 species. It’s mainly meant for studies of aging, so for each species, it includes variables such as lifespan, age of sexual maturity, adult body mass, typical body temperature, and metabolic rate. We can use this data to learn more about the relationships between each variable and the key outcome variable: lifespan."
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-    "text": "Data\nThis dataset contains features from 13,611 beans.\n\nData preview\n\n dry-beans.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nArea\nArea of the bean in the image, measured as the number of pixels it covers in the image\n\n\nPerimeter\nThe circumference of the bean, measured as the length of its border in pixels\n\n\nMajorAxisLength\nThe length of the bean’s major axis in pixels, i.e. the longest line that can be drawn from end to end of the bean\n\n\nMinorAxisLength\nThe length of the bean’s minor axis in pixels, i.e. the longest line, perpendicular to the major axis, that can be drawn across the bean\n\n\nAspectRation\nThe ratio of major axis to minor axis length\n\n\nEccentricity\nEccentricity of an ellipse with the same second moments of area as the bean\n\n\nConvexArea\nNumber of pixels in the smallest convex polygon that contains the bean\n\n\nEquivDiameter\nThe diameter of the circle with the same area of the bean\n\n\nExtent\nRatio of the area of the bean to the area of the smallest rectangle that contains it\n\n\nSolidity\nThe ratio of the bean’s area to its convex area (i.e. Area/ConvexArea)\n\n\nroundness\n4πA / P², where A is the bean area and P its perimeter\n\n\nCompactness\nEd/L, where Ed is the EquivDiameter and L is the major axis length\n\n\nShapeFactor1\nMajor axis length divided by area\n\n\nShapeFactor2\nMinor axis length divided by area\n\n\nShapeFactor3\n4A/(πL²), where A is area and L is the major axis length\n\n\nShapeFactor4\n4A/(πLl), where A is area, L is major axis length, and l is minor axis length\n\n\nClass\nVariety of the bean"
+    "text": "Data\nEach observation is one animal species. Note that many variables are missing for individual species, so you may need to restrict your analysis to complete observations.\n\nData preview\n\n anage.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nHAGRID\nA unique ID for each entry (the Human Ageing Genomic Resources ID)\n\n\nKingdom\nAlong with the following variables (phylum, class, order, family, genus, and species), this gives the taxonomic classification of the species. Species names are typically given as genus and species, e.g. Anaxyrus americanus is the species name for the American toad.\n\n\nPhylum\nPhylum of the species\n\n\nClass\nClass of the species\n\n\nOrder\nOrder of the species\n\n\nFamily\nFamily of the species\n\n\nGenus\nGenus of the species\n\n\nSpecies\nSpecies name\n\n\nCommon.name\nCommon name (i.e., the name used by ordinary people, not scientists) for the animal\n\n\nMaximum.longevity.yrs\nMaximum longevity (lifespan), in years\n\n\nBody.mass.g\nTypical adult body mass, in grams\n\n\nMetabolic.rate\nTypical resting metabolic rate (i.e., rate of energy use), in Watts\n\n\nTemperature\nTypical body temperature, in Kelvin"
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-    "text": "Questions\n\nUsing a multiclass classifier—such as a neural network, multiclass support vector machine, or kNN—build a classifier to predict bean variety from the available features. Choose the features you use carefully; because some features are calculated from others, some types of classifier may not need all features. Also choose your validation strategy carefully to ensure you can estimate your final model’s accuracy.\nExplore different ways to measure the accuracy of the classifier. Examining the confusion matrix may be useful to see what it is doing, but you should also produce useful summaries, such as accuracy, precision, recall, F1 score, and so on. Choose summaries that would be the most useful to bean producers.\nWhich varieties are easiest to confuse with each other? Which are most obviously different? Perhaps you can look up the varieties to see if the results make sense; if your classifiers can’t distinguish between two bean varieties that appear very different, something might be wrong."
+    "text": "Questions\n\nSome research has suggested that slowing the metabolic rate—for example, by intermittent fasting—may increase lifespan. Is this supported by the evidence? Develop a model for lifespan using metabolic rate, and interpret what it means.\nIs the relationship between metabolic rate and lifespan nonlinear, even after transformations? Use a nonparametric model to determine if a nonlinear fit is more appropriate.\nBuild a model to predict longevity using the available data. Evaluate its accuracy and report on any problems with the model.\nExplain whether your model supports the conclusion that changing an animal’s metabolic rate, such as through medical intervention, would cause its mean lifespan to change."
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-    "text": "References\nKoklu, M. and Ozkan, I.A., (2020), “Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques.” Computers and Electronics in Agriculture, 174, 105507. https://doi.org/10.1016/j.compag.2020.105507"
+    "text": "References\nThe AnAge Database of Animal Ageing and Longevity, at https://genomics.senescence.info/species/index.html. Data made available under the Creative Commons Attribution 3.0 Unported License."
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-    "text": "You’ve probably seen that insects seem to be attracted to artificial lights at night. Insects appear to be most attractive to blue and ultraviolet light, and less attracted by red or infrared light.\nPreviously, most outdoor high-intensity lighting—such as for street lights and parking lots—used sodium vapor lamps, a type of lamp that is very efficient for large lights but also produces a characteristic orange glow. Many cities and countries, however, are moving towards using light-emitting diodes (LEDs) in outdoor lighting, since they have become affordable and highly efficient. LED lights promise to require less maintenance and cost less to operate. Also, they are not limited to the orange glow of sodium vapor lamps—they can produce whiter light. Some research suggests this is beneficial for safety, since whiter light makes roads and sidewalks easier to see. But it can also have some harms.\nOne possible harmful effect is to insects. If LEDs can produce whiter or bluer light, they may be more attractive to insects at night, disrupting their circadian rhythms and potentially even leading to their deaths as they circle lights endlessly.\nThis data comes from a study intended to understand how what lights attract insects and what types of insects are most affected, to study the problem. The researchers set up light traps: each trap contained a light bulb and captured any insect attracted by the light to enter the trap.\nThe researchers conducted their experiment by setting up six light traps, each with a different light bulb. (One trap contained no bulb, as a control.) The traps were set up each evening at sunset and then checked in the morning after sunrise, and the insects collected identified and counted.\nTraps were set up at three different sites (one urban, two rural). Relevant weather conditions were recorded. The experiment was repeated 32 times: 16 times at the urban site and 8 times at each rural site. This allowed the researchers to rotate which location (within an individual site) each trap was placed in, and allowed them to collect data under varying weather and moon phases. (Since the moon is bright in the night sky, the presence of a bright moon could presumably affect the results.)"
+    "text": "Ecologists and evolutionary biologists have long investigated links between diversity and geography. For example, as the area of a habitat increases, the number of different species found within it increases—a seemingly obvious finding, but the exact shape of the relationship has been found to relate to the rates of immigration and extinction in the habitat, predator-prey behaviors, and so on.\nA similar relationship would seem likely to exist within a species. A species that lives over a small area might have low genetic diversity, because individuals frequently interbreed and there is little opportunity for some individuals to evolve separately from others. A species that lives over a wide area might have high genetic diversity, both because the varied habitat in which it lives means different individuals experience different natural selection pressures, and because individuals far apart may never be able to interbreed.\nThe exact nature of this relationship could be used to test hypotheses about evolution and natural selection. This dataset comes from a paper that explored the relationship in birds, which have widely varying geographic ranges and hence could be a good group to work with."
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-    "text": "You’ve probably seen that insects seem to be attracted to artificial lights at night. Insects appear to be most attractive to blue and ultraviolet light, and less attracted by red or infrared light.\nPreviously, most outdoor high-intensity lighting—such as for street lights and parking lots—used sodium vapor lamps, a type of lamp that is very efficient for large lights but also produces a characteristic orange glow. Many cities and countries, however, are moving towards using light-emitting diodes (LEDs) in outdoor lighting, since they have become affordable and highly efficient. LED lights promise to require less maintenance and cost less to operate. Also, they are not limited to the orange glow of sodium vapor lamps—they can produce whiter light. Some research suggests this is beneficial for safety, since whiter light makes roads and sidewalks easier to see. But it can also have some harms.\nOne possible harmful effect is to insects. If LEDs can produce whiter or bluer light, they may be more attractive to insects at night, disrupting their circadian rhythms and potentially even leading to their deaths as they circle lights endlessly.\nThis data comes from a study intended to understand how what lights attract insects and what types of insects are most affected, to study the problem. The researchers set up light traps: each trap contained a light bulb and captured any insect attracted by the light to enter the trap.\nThe researchers conducted their experiment by setting up six light traps, each with a different light bulb. (One trap contained no bulb, as a control.) The traps were set up each evening at sunset and then checked in the morning after sunrise, and the insects collected identified and counted.\nTraps were set up at three different sites (one urban, two rural). Relevant weather conditions were recorded. The experiment was repeated 32 times: 16 times at the urban site and 8 times at each rural site. This allowed the researchers to rotate which location (within an individual site) each trap was placed in, and allowed them to collect data under varying weather and moon phases. (Since the moon is bright in the night sky, the presence of a bright moon could presumably affect the results.)"
+    "text": "Ecologists and evolutionary biologists have long investigated links between diversity and geography. For example, as the area of a habitat increases, the number of different species found within it increases—a seemingly obvious finding, but the exact shape of the relationship has been found to relate to the rates of immigration and extinction in the habitat, predator-prey behaviors, and so on.\nA similar relationship would seem likely to exist within a species. A species that lives over a small area might have low genetic diversity, because individuals frequently interbreed and there is little opportunity for some individuals to evolve separately from others. A species that lives over a wide area might have high genetic diversity, both because the varied habitat in which it lives means different individuals experience different natural selection pressures, and because individuals far apart may never be able to interbreed.\nThe exact nature of this relationship could be used to test hypotheses about evolution and natural selection. This dataset comes from a paper that explored the relationship in birds, which have widely varying geographic ranges and hence could be a good group to work with."
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-    "text": "Data\nEach row of data represents one trap on one night, and records the light bulb used in that trap, its location, the weather conditions, and the insects collected in that trap.\n\nData preview\n\n bug-attraction.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nLight Type\nType of light bulb used to attract insects. No means no bulb at all; A, B, and C are all Philips light bulbs whose color can be adjusted by the user; LED is a commercial white LED bulb; and CFL is a commercial compact fluorescent bulb.\n\n\nModel Lamp\nMore detailed description of the light bulb. For A, B, and C, describes the color temperature (K) the bulbs were set to.\n\n\nLocation\nLocation the light trap was placed. Site codes starting with B are urban (the UCLA Botanical Garden); site codes L and S were rural locations (a UCLA field station and a UCLA ranch). Each site has six separate traps, designated by a number.\n\n\nSite\nSite. BG = botanical garden, LK = La Kretz field station, Stunt = stunt ranch.\n\n\nStandardized Moon\nStandardized variable representing the percentage of the moon visible on the night chosen (i.e., the moon phase).\n\n\nStandardized Mean Temp\nTemperature on the night, standardized.\n\n\nStandardized Mean Humidity\nHumidity on the night, standardized.\n\n\nStandardized Max Wind\nMaximum wind speed on the night, standardized.\n\n\n% Moon Visible\nPercentage of moon visible (moon phase).\n\n\nStart Tem\nTemperature at the beginning of the night (Celsius)\n\n\nEnd Temp\nTemperature at the end of the experiment (Celsius)\n\n\nMean Temp\nMean temperature (Celsius)\n\n\nHumid Start\nRelative humidity at the start of the experiment (%)\n\n\nHumid End\nRelative humidity at the end of the experiment (%)\n\n\nMean Humidity\nMean humidity (%)\n\n\nMax Wind\nMaximum recorded wind speed (m/s)\n\n\nIllumination\nIllumination produced by the light (lux)\n\n\nDiptera\nNumber of insects of the order Diptera captured in the trap\n\n\nHymenoptera\nNumber of insects of the order Hymenoptera captured in the trap\n\n\nHemiptera\nNumber of insects of the order Hemiptera captured in the trap\n\n\nPsocoptera\nNumber of insects of the order Psocoptera captured in the trap\n\n\nColeoptera\nNumber of insects of the order Coleoptera captured in the trap\n\n\nCollembola\nNumber of insects of the order Collembola captured in the trap\n\n\nArachnid\nNumber of insects of the order Arachnid captured in the trap\n\n\nThysanura\nNumber of insects of the order Thysanura captured in the trap\n\n\nIsoptera\nNumber of insects of the order Isoptera captured in the trap\n\n\nLepidoptera\nNumber of insects of the order Lepidoptera captured in the trap\n\n\nNeuroptera\nNumber of insects of the order Neuroptera captured in the trap\n\n\nLarave\nNumber of insects of the order Larave captured in the trap\n\n\nOrthoptera\nNumber of insects of the order Orthoptera captured in the trap\n\n\nUnident\nNumber of unidentified insects captured in the trap\n\n\nTotal\nTotal number of insects captured in the trap\n\n\nOther Orders\nNumber of insects of other identified orders captured in the trap"
+    "text": "Data\nThis data includes 387 bird species, along with two measures of their genetic diversity, a measure of the size of their breeding range, their body mass, and whether they are migratory or resident birds. The breeding ranges vary in size from a just few islands in the Seychelles, for the Seychelles warbler (Acrocephalus sechellensis), to enormous ranges of the Southern Ocean for the grey-headed albatross (Thalassarche chrysostoma).\n\nData preview\n\n bird-diversity.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nSpecies\nBird species name\n\n\nFamily\nFamily of birds to which this species belongs\n\n\nHeterozygosity\nA measure of genetic richness. Birds (and most animals) have two copies of each chromosome; expected heterozygosity is the probability that for a randomly selected gene, these two copies will be different versions (alleles) of the gene.\n\n\nAllelic richness\nThe average number of different alleles (versions of a gene) for each gene.\n\n\nBreeding range size\nSize of the spatial range over which this bird breeds, in units of 10,000 square km\n\n\nBody mass\nAverage body mass of the species (grams)\n\n\nLatitude\nMid-point latitude of the species breeding range. (All latitudes are positive, even those in the southern hemisphere.)\n\n\nMigratory status\nIs this species migratory or does it stay resident in one area?\n\n\nReferences\nReferences for the data for this species"
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-    "text": "Questions\nThese are adapted from questions used in notes for 36-707.\n\nWe are interested in the total number of insects collected in each trap (the Total variable) and how it relates to the light bulb type (Light Type). We also know that the location (Location), moon phase (% Moon Visible), mean night temperature (Mean Temp), and mean humidity (Mean Humidity) might affect insect activity. Conduct an EDA of these variables to determine what relationships the predictors may have with the response.\nConsider the study design. Because six traps were placed out each night, each with a different bulb (or no bulb), the traps were exposed to the same location, moon phase, temperature, and humidity. No bulb type has data from moon phases that are systematically different from the other bulbs, or was used on nights that were warmer than those for the other bulbs, or anything like that. This is known as a balanced design.\nComment on what this design choice means. Can location, moon phase, mean temperature, or mean humidity be confounding variables that would bias our estimates of the effect of bulb type? Do we need to incorporate them into our models to obtain an unbiased estimate of the causal effect of bulb type?\nObtain the mean number of insects trapped by each bulb. Display the means in a table. Which bulb attracts the fewest insects?\nNote there is one missing count (recorded as NA) that you may need to remove.\nSelect a GLM for the total number of insects collected as a function of bulb type, location, moon phase, temperature, and humidity. Use your EDA to guide your model choice, and produce any necessary diagnostic plots to validate your choice.\nProduce a table of coefficients estimated from your final model, including standard errors. Interpret, in words, the meaning of the coefficient for temperature, giving a 95% confidence interval for the size of the effect.\nConsider a night where the moon is 60% visible, the humidity is 60%, and the mean temperature is 15 °C. (These are roughly the average values in the data.) Produce a table of the predicted number of insects trapped by each bulb type at the La Kretz station on a night like this, giving 95% confidence intervals for each prediction. Does your result match the table from part 3?"
+    "text": "Questions\n\nBody size and latitude are thought to be related to genetic diversity for reasons unrelated to breeding range size. Regress heterozygosity against breeding range size, body mass, and latitude. Examine the shape of the relationship, which is thought not to be linear and suspected to have an asymptote. Does the relationship depend on whether the species are migratory? Compare this to the shape you find using allelic richness instead of heterozygosity as the outcome.\nSpecies in this dataset are grouped into families; by definitions, species within a family are more similar than species in two different families. This may have effects on the assumptions of the regression. What assumptions may be violated, and what effect would this have on your results? (Note that the original paper accounted for this effect by taking into account the species’ phylogenetic trees.)\nFit a hierarchical model to account for this effect, using family to define the hierarchy. How do your results change?"
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-    "text": "References\nLongcore, Aldern, Eggers, Flores, Franco, Hirshfield-Yamanishi, Petrinec, Yan, and Barroso (2015). “Tuning the white light spectrum of light emitting diode lamps to reduce attraction of nocturnal arthropods”. Philosophical Transactions of the Royal Society B 370: 20140125. https://doi.org/10.1098/rstb.2014.0125"
+    "text": "References\nFan H, Zhang Q, Rao J, Cao J, Lu X (2019) Genetic diversity-area relationships across bird species. The American Naturalist, volume 194, number 5. https://doi.org/10.1086/705346\nFan H, Zhang Q, Rao J, Cao J, Lu X (2019) Data from: Genetic diversity-area relationships across bird species. Dryad Digital Repository. https://doi.org/10.5061/dryad.9p380m9"
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-    "text": "The Data Repository curates interesting datasets for use in statistics and data science education. Each dataset is supported by a story describing its origin and application, and a set of interesting questions that can be answered using the data. This means:\n\nEvery dataset has context in a scientific field, pop culture, or daily life.\nBeyond context, datasets are interesting. They feature more than just a dozen observations from an antiquated scientific study — many feature thousands of observations of dozens of variables, and answer questions interesting to a wide audience.\nJust like in science, some datasets give null results.\nInstructors can easily build lessons and assignments from the suggested questions.\n\nDatasets are organized by broad subject areas on the left, or you can browse a sortable list of all datasets."
+    "text": "The simplest way to submit data is to use our online submission form. Provide the requested information and hit Submit; we’ll review the submission and eventually add it to the site, if it’s suitable.\nOur overall goal is to provide a collection of useful, real-world datasets from a variety of application areas that can be used as in-class examples, homework assignments, or course projects. The repository can always use new datasets, so we welcome submissions. Datasets should meet a few requirements:\nIf you’re familiar with R Markdown or Quarto, you can instead prepare the data description page yourself from a template. Read on for detailed instructions."
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-    "text": "Here are some good outside sources for finding datasets suitable for classes:\n\nData is Plural, a weekly newsletter of interesting public datasets.\nSCORE Sports Data Repository, a collection of sports datasets for classroom use\nStanford Large Network Dataset Collection\nRoyal Society Open Science, an open-access general science journal whose papers have Data Availability statements and often have public data\nScientific Data, a journal publishing descriptions of open datasets (often large and with unusual structure)\nData in Brief, another journal publishing descriptions of open datasets\nOpen Case Studies, a collection of data analysis case studies showing analyses from conception to conclusion\nUCI Machine Learning Repository, containing hundreds of datasets suitable for machine learning tasks\nTidyTuesday, a project collecting datasets for weekly data wrangling and visualization events"
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+    "text": "Getting Started with Quarto\nThis website is built using the Quarto system and automatically rendered into a website. Data description pages can be made directly from a template file we provide.\nYou can get the template in two ways:\n\nCopy the _dataset-template.qmd file from our GitHub repository and save it on your computer. Once you’re done, you can email us the file and the data.\nFork our GitHub repository into your own GitHub account and edit it like any other Git repository. Once you’re done, you can submit a pull request."
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+    "text": "The Dataset Template\nThe dataset template is a Quarto file; Quarto is much like R Markdown and is supported by recent versions of RStudio. Quarto files can contain R code, just like R Markdown, so your data description can include graphics and tables generated by R code embedded in the file, if you think this would be helpful to illustrate important features of the data. As you fill out the template, you can use RStudio’s “Render” button to see a preview of the finished page.\nThe template asks for:\n\nBasic metadata, such as the statistical methods applicable to the data, a short title, and your name. (All submissions are credited with the name of the submitter.)\nA description of the problem. This should be sufficient to motivate the analysis and help students understand the setting.\nA description of the data and the variables. It should be clear what each row of data represents and what all variables mean. Units of measure should be included whenever possible.\nReferences to the original source. If the source is an academic paper and the data is deposited in a third-party repository (such as Zenodo, Figshare, or Dryad), include references to both the paper and the archived dataset. When available, include the DOIs of the references.\n\nThe template is meant to prevent a common problem with course datasets: they get passed down from instructor to instructor, and eventually all information about the original source is lost. The dataset may be presented to students without context or important details (like units), and the instructor may not be able to find the original data or references to answer questions about the data. By providing sufficient detail, we can make datasets reusable for years to come."
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+    "text": "Submitting\nOnce you’ve filled in the template, you can email the template file, the data, and any cleaning scripts to the repository editor (currently Alex Reinhart).\nAlternately, submit a pull request to our repository on GitHub. Save the Qmd with a meaningful name and place it in one of the top-level category directories (like medicine/ or astronomy/). Place the data files in data/. Double-check that you’ve committed everything (and nothing extraneous) and open a pull request as usual."
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-    "text": "Psychologists and food scientists have, in recent years, produced a great deal of research on a simple question: Do people eat more food if their meals are served on larger plates?\nThis question is of real practical importance. The ways food is packaged and presented undoubtedly affect how we eat it, and with obesity rates high across the Western world, simple ways to reduce overconsumption of food could have major public health benefits. Perhaps people overconsume food because they tend to eat most of what’s on their plates, leading them to eat more when the plate is large; if so, general advice to buy smaller plates could have a nontrivial effect.\nSeveral experiments suggested this hypothesis is true, and that reducing plate size would help. But some of these experiments were conducted by Cornell University’s Food and Brand Lab, and starting in 2017 researchers started noticing serious problems with the lab’s publications, including miscalculated test statistics, inconsistencies in results, and incorrect descriptions of experimental procedures; since then, many of the lab’s papers have been retracted entirely, and the lab’s director, Brian Wansink, resigned from Cornell. It is not clear which conclusions can be trusted and which cannot.\nThis study sought to settle the question by conducting a large, carefully designed experiment, whose design and analysis was preregistered so it could not be manipulated once data was collected. In the study, participants were recruited and told they were participating in an experiment “examining the impact of time of the day on a range of mental processes,” and that they happened to be scheduled for a lunchtime session. They were told not to eat anything for three hours before the session; upon arriving, they completed various survey tasks, then were invited to serve themselves from a food trolley. The size of plate they were provided (small or large) was randomly assigned, and the exact quantity of food they consumed was measured (by measuring what was left in the trolley and on their plate). The session was also video recorded by hidden cameras, so researchers could count how many bites the participants took and the rate at which they ate."
+    "text": "Estimating the properties of galaxies, and even where they are, is a challenging process. The Rubin Observatory, a sky survey telescope located in Chile, will once it becomes operational image tens of billions of astronomical objects, the vast majority of which will be galaxies that have never been imaged before. Analyses of these data will require sophisticated methodologies, ones that will allow us to first determine where the galaxies are (i.e., how far away they are), and then conditional on the distance, how massive they are. Given galaxy distance and mass data, we can test theories of how the Universe evolves, by comparing simulated galaxy data with these data.\nThe Buzzard-V1.0 simulation was used to generate a realistic sample of Rubin Observatory data. In this dataset are measurements for 111,172 galaxies. Developers used these data to benchmark, e.g., methods for estimating galaxy distance. Here, we can assume the distance has been estimated well, and use these data to try to model galaxy mass as a function of brightness and distance."
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-    "text": "Psychologists and food scientists have, in recent years, produced a great deal of research on a simple question: Do people eat more food if their meals are served on larger plates?\nThis question is of real practical importance. The ways food is packaged and presented undoubtedly affect how we eat it, and with obesity rates high across the Western world, simple ways to reduce overconsumption of food could have major public health benefits. Perhaps people overconsume food because they tend to eat most of what’s on their plates, leading them to eat more when the plate is large; if so, general advice to buy smaller plates could have a nontrivial effect.\nSeveral experiments suggested this hypothesis is true, and that reducing plate size would help. But some of these experiments were conducted by Cornell University’s Food and Brand Lab, and starting in 2017 researchers started noticing serious problems with the lab’s publications, including miscalculated test statistics, inconsistencies in results, and incorrect descriptions of experimental procedures; since then, many of the lab’s papers have been retracted entirely, and the lab’s director, Brian Wansink, resigned from Cornell. It is not clear which conclusions can be trusted and which cannot.\nThis study sought to settle the question by conducting a large, carefully designed experiment, whose design and analysis was preregistered so it could not be manipulated once data was collected. In the study, participants were recruited and told they were participating in an experiment “examining the impact of time of the day on a range of mental processes,” and that they happened to be scheduled for a lunchtime session. They were told not to eat anything for three hours before the session; upon arriving, they completed various survey tasks, then were invited to serve themselves from a food trolley. The size of plate they were provided (small or large) was randomly assigned, and the exact quantity of food they consumed was measured (by measuring what was left in the trolley and on their plate). The session was also video recorded by hidden cameras, so researchers could count how many bites the participants took and the rate at which they ate."
+    "text": "Estimating the properties of galaxies, and even where they are, is a challenging process. The Rubin Observatory, a sky survey telescope located in Chile, will once it becomes operational image tens of billions of astronomical objects, the vast majority of which will be galaxies that have never been imaged before. Analyses of these data will require sophisticated methodologies, ones that will allow us to first determine where the galaxies are (i.e., how far away they are), and then conditional on the distance, how massive they are. Given galaxy distance and mass data, we can test theories of how the Universe evolves, by comparing simulated galaxy data with these data.\nThe Buzzard-V1.0 simulation was used to generate a realistic sample of Rubin Observatory data. In this dataset are measurements for 111,172 galaxies. Developers used these data to benchmark, e.g., methods for estimating galaxy distance. Here, we can assume the distance has been estimated well, and use these data to try to model galaxy mass as a function of brightness and distance."
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-    "text": "Data\nEach row of the dataset is one participant in the study. Beyond quantities of food consumed and the rate at which it was consumed, some basic demographic information was collected, as well as several standard psychological tasks.\nNote: A value of -999 means that value was not recorded; a value of -888 on a survey question means the participant answered that they would “rather not say.”\n\nData preview\n\n plate-size.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nDate\nDate this participant participated\n\n\nID\nParticipant ID number\n\n\nplate.size\n0 = small plate, 1 = large plate\n\n\nkcal.consumed\nTotal number of calories consumed\n\n\ng.consumed\nMass of food the participant ate (grams)\n\n\ng.served\nMass of food the participant put on their plate (grams)\n\n\ng.remaining\nMass of food left on participant’s plate (grams)\n\n\nservings\nNumber of times the participant served themself food\n\n\nmeal.duration\nTime from the first bite to the last bite (minutes)\n\n\nn.bites\nNumber of bites taken\n\n\nbite.size\nAverage bite size (total food mass / number of bites, grams)\n\n\neat.rate\nTime taken per bite (meal duration / number of bites)\n\n\nbite.rate\nNumber of bites per minute\n\n\nhighest.qual\nHighest educational qualification. 1 = none, 2 = up to 4 GCSEs, 3 = 5 or more GCSEs or 1 A-level, 4 = 2 or more A-levels, 5 = bachelor’s degree, 6 = post-graduate degree, 7 = rather not say.\n\n\nhighest.qual.split\nHighest educational qualification discretized\n\n\nincome\nTotal household income per year, before tax (self-reported). 1 = up to £4,499; 2 = up to £6,499; 3 = up to £7,499; 4 = up to £9,499; 5 = up to £11,499; 6 = up to £13,499; 7 = up to £15,499; 8 = up to £17,499; 9 = up to £24,999; 10 = up to £29,999; 11 = up to £39,999; 12 = up to £49,999; 13 = up to £74,999; 14 = up to £99,999; 15 = over £100,000\n\n\nincome.med.split\n0 = total household income below median; 1 = total household income above median.\n\n\nimd\nIndex of Multiple Deprivation of the area where the participant lives. Measures income, unemployment, education, crime, and other factors related to socioeconomic status and the living environment. Small values mean greater deprivation.\n\n\nimd.med.split\n0 = IMD below median value; 1 = IMD above median value.\n\n\nssrt\nStop Signal Task response time (milliseconds). A task intended to measure participants’ response inhibition and impulsivity; slow response times indicate poor impulse control.\n\n\nBIS-TOTAL\nTotal score from the Barratt Impulsiveness Scale. Higher scores indicate greater impulsiveness.\n\n\nBIS-Q1\nBIS item 1: ‘I plan tasks carefully.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q2\nBIS item 2: ‘I do things without thinking.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q3\nBIS item 3: ‘I make-up my mind quickly.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q4\nBIS item 4: ‘I am happy-go-lucky.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q5\nBIS item 5: ‘I don’t pay attention.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q6\nBIS item 6: ‘I have racing thoughts.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q7\nBIS item 7: ‘I plan trips well ahead of time.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q8\nBIS item 8: ‘I am self controlled.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q9\nBIS item 9: ‘I concentrate easily.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q10\nBIS item 10: ‘I save regularly.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q11\nBIS item 11: ‘I squirm at plays or lectures.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q12\nBIS item 12: ‘I am a careful thinker.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q13\nBIS item 13: ‘I plan for job security.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q14\nBIS item 14: ‘I say things without thinking.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q15\nBIS item 15: ‘I like to think about complex problems.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q16\nBIS item 16: ‘I change jobs.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q17\nBIS item 17: ‘I act on impulse.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q18\nBIS item 18: ‘I get easily bored when solving thought problems.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q19\nBIS item 19: ‘I act on the spur of the moment.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q20\nBIS item 20: ‘I am a steady thinker.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q21\nBIS item 21: ‘I change residences.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q22\nBIS item 22: ‘I buy things on impulse.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q23\nBIS item 23: ‘I can only think about one thing at a time.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q24\nBIS item 24: ‘I change hobbies.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q25\nBIS item 25: ‘I spend or charge more than I earn.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q26\nBIS item 26: ‘I often have extraneous thoughts when thinking.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q27\nBIS item 27: ‘I am more interested in the present than the future.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q28\nBIS item 28: ‘I am restless at the theater or lectures.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q29\nBIS item 29: ‘I like puzzles.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q30\nBIS item 30: ‘I am future oriented.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nline.task.correct\nNumber of correct answers to a line orientation task, out of 24 possible. Measures ability to orient objects in space.\n\n\ngender\nGender. 1 = male, 2 = female, 3 = other, 4 = rather not say.\n\n\nage\nAge (years)\n\n\nethnicity\nEthnicity. 1 = white; 2 = mixed white and black, Asian, Caribbean, or other; 3 = Asian; 4 = Black; 5 = other; 6 = rather not say.\n\n\nethnicity.dicot\nEthnicity dicotomized. 0 = white, 1 = non-white.\n\n\nBMI\nBody mass index\n\n\nhunger\nHow hungry the participant reported being before lunch. 100 point scale. 0 = not at all, 100 = very much.\n\n\nfullness\nHow full the participant reported being before lunch. 100 point scale.\n\n\nlike.food\nHow much the participant reported liking the food provided in the study. 100 point scale.\n\n\nfasting\nWhether the participant fasted for 3 hours before the study, as requested (yes/no)."
+    "text": "Data\nThe dataset contains measures of magnitude and magnitude uncertainty in six astronomical bands (u for ultraviolet, g for green, r for red, i for infrared, and z and y for two additional infrared bands). Magnitude is a logarithmic measure of brightness, with an increase of 5 representing a decrease in brightness by a factor of 100, and with a value of zero being represented (roughly) by how the star Vega appears in the night sky. In addition, there is a redshift measured for each galaxy; it represents by how much light from the galaxy is stretched (by the expansion of Universe) as it travels to us. Thus higher redshifts represent larger distances. The last measurement is log.mass, which is the base-10 logarithm of the galaxy stellar mass in units of solar mass; for instance, log.mass = 10 means that the galaxy has a mass 10 billion times that of the Sun.\n\nData preview\n\n Buzzard_DC1.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\nVariable\nDescription\n\n\n\n\nu\nGalaxy magnitude in Rubin u band (320.5-393.5 nm)\n\n\ng\nGalaxy magnitude in Rubin g band (401.5-551.9 nm)\n\n\nr\nGalaxy magnitude in Rubin r band (552.0-691.0 nm)\n\n\ni\nGalaxy magnitude in Rubin i band (691.0-818.0 nm)\n\n\nz\nGalaxy magnitude in Rubin z band (818.0-923.5 nm)\n\n\ny\nGalaxy magnitude in Rubin y band (923.8-1084.5 nm)\n\n\nu.err\nUncertainty for u-band magnitude\n\n\ng.err\nUncertainty for g-band magnitude\n\n\nr.err\nUncertainty for r-band magnitude\n\n\ni.err\nUncertainty for i-band magnitude\n\n\nz.err\nUncertainty for z-band magnitude\n\n\ny.err\nUncertainty for y-band magnitude\n\n\nlog.mass\nGalaxy stellar mass (log-base-10 solar masses)\n\n\nredshift\nGalaxy redshift"
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-    "text": "Questions\n\nThe simplest question is also the one that directly answers the study’s main question: Did participants given a larger plate eat more, on average, than those given a smaller plate?\nPerhaps the effect of plate size varies depending on impulsivity (measured by the Barratt Impulsiveness Scale and the Stop Signal Task) or socioeconomic status (measured by income, education, and the Index of Multiple Deprivation). One could imagine, for example, that people who regularly struggle to get enough food might be more affected by the desire to eat everything on their plate. Conduct an analysis to see if the effect of plate size differs depending on these factors.\nThe study was designed to have the sample size needed to get 80% statistical power to detect a difference of d = 0.5 standard deviations in the amount of food consumed between the two groups. The power analysis did not determine the sample size needed to detect if the effect of plate size differs depending on other factors. Use simulation to determine the power of the study to detect such differences. Is it capable of reliably detecting them?"
+    "text": "Questions\nAs noted above, the idea here is to learn a statistical association between measures of magnitude and distance, and galaxy mass.\nOne wrinkle here that analysts can exploit is that the data contain standard error estimates for the magnitudes (though not for redshift, for which, in practice, the error would be \\(\\sim 10^{-5}\\))."
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     "section": "References",
-    "text": "References\nDaina Kosīte, Laura M. König, Katie De-loyde, Ilse Lee, Emily Pechey, Natasha Clarke, Olivia Maynard, Richard W. Morris, Marcus R. Munafò, Theresa M. Marteau, Paul C. Fletcher & Gareth J. Hollands (2019). “Plate size and food consumption: a pre-registered experimental study in a general population sample,” International Journal of Behavioral Nutrition and Physical Activity 16 no. 75. https://doi.org/10.1186/s12966-019-0826-1\nData available from the Open Science Framework: https://osf.io/k74cu/"
+    "text": "References\nSchmidt, Malz, Soo, Almosallam, Brescia, Cavuoti, Cohen-Tanugi, Connolly, DeRose, Freeman, Graham, Iyer, Jarvis, Kalmbach, Kovacs, Lee, Longo, Morrison, Newman, Nourbakhsh, Nuss, Pospisil, Tranin, Wechsler, Zhou, Izbicki, (The LSST Dark Energy Science Collaboration). “Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)”. Monthly Notices of the Royal Astronomical Society 499, December 2020, pages 1587–1606. https://doi.org/10.1093/mnras/staa2799"
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-    "text": "Some research has suggested that groups of people working on a task can do better if the group is more diverse, since diverse group members can suggest more creative ideas and make better decisions. At the same time, diverse groups can have more conflict than less diverse groups, possibly eliminating those benefits.\nThis dataset is from a study that attempted to understand these factors and how they relate to the testosterone levels of members of the group. People with high levels of testosterone tend to be competitive and may try to dominate their groups, leading to conflict, while people with low testosterone may be more cooperative. So do groups with low testosterone work better together, and benefit more from diversity, than those with high testosterone?\nThe study involved 370 students working on their Master of Business Administration degrees at Columbia Business School. The students were randomly assigned to groups of three to six people, and their testosterone levels were measured. The groups then did a week-long project where they pretended to run blood-testing laboratories, with the goal of making their laboratories perform better than those run by other groups in the class. Group members had to collaborate with each other to decide on their strategy. At the end of the project, the researchers recorded how well each group’s laboratories did, how much money they made, and so on.\nThe result, according to the study, was that\n\nThe findings suggest that diversity is beneficial for performance, but only if group-level testosterone is low; diversity has a negative effect on performance if group-level testosterone is high."
+    "text": "For many reasons, it’s important for students to succeed during their first year of college. Success during the first year affects whether students will stay to complete the rest of their degree, and college GPA seems to predict future success in jobs. Universities often invest large amounts of time and money to help students adapt to college during their first year, through special programs, tutoring, orientation sessions, and so on.\nBesides programs offered by universities, we can also ask what students can do to improve their chances. One potential factor is sleep. College students often have poor sleep habits, staying up late and sleeping short hours, and a great deal of research suggests that lack of sleep can harm cognitive performance. It seems reasonable, then, to study how sleep habits in college students are related to their grade point averages (GPAs).\nTo assess this, this study recruited first-year students at three universities:\n\nCarnegie Mellon University (CMU), a STEM-focused private university\nThe University of Washington (UW), a large public university\nNotre Dame University (ND), a private Catholic university\n\nEach student participant was given a device to track their sleep patterns for a month early in their spring academic term. The researchers then obtained grade data from the university registrars, along with demographic information about the students. The demographic information included other factors that might be related to the students’ GPAs, such as whether they were first-generation students whose parents never attended college, so these factors could be controlled for."
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-    "text": "Some research has suggested that groups of people working on a task can do better if the group is more diverse, since diverse group members can suggest more creative ideas and make better decisions. At the same time, diverse groups can have more conflict than less diverse groups, possibly eliminating those benefits.\nThis dataset is from a study that attempted to understand these factors and how they relate to the testosterone levels of members of the group. People with high levels of testosterone tend to be competitive and may try to dominate their groups, leading to conflict, while people with low testosterone may be more cooperative. So do groups with low testosterone work better together, and benefit more from diversity, than those with high testosterone?\nThe study involved 370 students working on their Master of Business Administration degrees at Columbia Business School. The students were randomly assigned to groups of three to six people, and their testosterone levels were measured. The groups then did a week-long project where they pretended to run blood-testing laboratories, with the goal of making their laboratories perform better than those run by other groups in the class. Group members had to collaborate with each other to decide on their strategy. At the end of the project, the researchers recorded how well each group’s laboratories did, how much money they made, and so on.\nThe result, according to the study, was that\n\nThe findings suggest that diversity is beneficial for performance, but only if group-level testosterone is low; diversity has a negative effect on performance if group-level testosterone is high."
+    "text": "For many reasons, it’s important for students to succeed during their first year of college. Success during the first year affects whether students will stay to complete the rest of their degree, and college GPA seems to predict future success in jobs. Universities often invest large amounts of time and money to help students adapt to college during their first year, through special programs, tutoring, orientation sessions, and so on.\nBesides programs offered by universities, we can also ask what students can do to improve their chances. One potential factor is sleep. College students often have poor sleep habits, staying up late and sleeping short hours, and a great deal of research suggests that lack of sleep can harm cognitive performance. It seems reasonable, then, to study how sleep habits in college students are related to their grade point averages (GPAs).\nTo assess this, this study recruited first-year students at three universities:\n\nCarnegie Mellon University (CMU), a STEM-focused private university\nThe University of Washington (UW), a large public university\nNotre Dame University (ND), a private Catholic university\n\nEach student participant was given a device to track their sleep patterns for a month early in their spring academic term. The researchers then obtained grade data from the university registrars, along with demographic information about the students. The demographic information included other factors that might be related to the students’ GPAs, such as whether they were first-generation students whose parents never attended college, so these factors could be controlled for."
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-    "text": "Data\nThe first data file contains individual data: the age, gender, and ethnicity of each student, along with their testosterone and cortisol levels. The second file reports the performance of the teams on their projects. There were 370 students in 74 groups.\n\nData preview\n\n hormone-diversity-individual.csv \n\n\n\n\n\n\n hormone-diversity-teams.csv \n\n\n\n\n\n\n\nVariable descriptions\n\nhormone-diversity-individual.csv\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nID\nParticipant ID number\n\n\nteam.id\nID number of the team this participant belonged to\n\n\nAge\nAge, in years\n\n\nGender\nGender (Male or Female)\n\n\nEthnicity\nEthnicity of the participant\n\n\nCortisol\nParticipant’s cortisol levels, nMol/L\n\n\nTestosterone\nParticipant’s testosterone levels, pg/mL\n\n\nlog.cortisol\nNatural logarithm of the participant’s cortisol level\n\n\nlog.testosterone\nNatural logarithm of the participant’s testosterone level\n\n\nCountry\nCountry of citizenship of the participant\n\n\n\n\nhormone-diversity-teams.csv\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nteam.id\nTeam ID number\n\n\nteam.size\nNumber of people on the team\n\n\nfinal.performance\nThe team’s final performance score\n\n\ntime.of.day\nThe time of day the team’s hormone sample was collected (hh.mm)\n\n\nfemales\nNumber of females in the group\n\n\nfinal.cash\nTotal cash earned by the team\n\n\nfinal.contracts\nTotal number of contracts won by the team\n\n\nfinal.reorders\nTotal number of reorders won by the team\n\n\nfinal.rank\nTeam’s final rank at the end of the project, relative to other teams in their class section\n\n\ninterim.performance\nSame as above, but measured at Day 5 of the study (missing for some teams)\n\n\ninterim.cash\n\n\n\ninterim.contracts\n\n\n\ninterim.reorders\n\n\n\ninterim.rank"
+    "text": "Data\nThere were a total of 634 participants in this study, who each received a Fitbit to track their sleep and physical activity. Students’ GPAs were collected from the university registrar. Each data row represents one participant.\nUsing the Fitbit data, the researchers extracted sleep “episodes”, which are simply time periods where the Fitbit reported the participant as sleeping. They only counted episodes at least 20 minutes long, separated by at least 5 minutes from other episodes. Gaps smaller than 5 minutes were ignored, so if a student slept for several hours, woke for a few minutes, and then slept again, this was counted as a single episode.\nThe longest sleep episode between noon of one day and noon of the next was considered the “main” episode; anything else was considered “daytime sleep”. For example, if a student slept from 4pm to 5pm, then again from 2am to 8am, the 2-8am window was the “main” episode (6 hours long), and the 4-5pm window was counted as 1 hour of daytime sleep. The beginning of the main episode was counted as the student’s “bedtime”, so in this example, the student’s bedtime was 2am.\nThe data is divided into five separate studies, by university and semester:\n\n\n\nStudy\nUniversity\nSemester\n\n\n\n\n1\nCarnegie Mellon University\nSpring 2018\n\n\n2\nUniversity of Washington\nSpring 2018\n\n\n3\nUniversity of Washington\nSpring 2019\n\n\n4\nNotre Dame University\nSpring 2016\n\n\n5\nCarnegie Mellon University\nSpring 2017\n\n\n\n\nData preview\n\n cmu-sleep.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nsubject_id\nUnique ID of the subject\n\n\nstudy\nStudy number, as shown in the table above\n\n\ncohort\nCodename of the cohort that the subject belongs to\n\n\ndemo_race\nBinary label for underrepresented and non-underrepresented students (underrepresented = 0, non-underpresented = 1). Students were considered underrepresented if either parent was Black, Hispanic or Latino, Native American, or Pacific Islander. Students were non-underrepresented if neither parent was from an underrepresented category (i.e., both parents had White or Asian ancestry).\n\n\ndemo_gender\nGender of the subject (male = 0, female = 1), as reported by their institution.\n\n\ndemo_firstgen\nFirst-generation status (non-first gen = 0, first-gen = 1). Students were considered first-generation if neither parent completed any college (i.e., high school diploma or less)\n\n\nbedtime_mssd\nMean successive squared difference of bedtime. This measures bedtime variability, and is calculated as the average of the squared difference of bedtime on consecutive nights. For example, if we had four successive nights of sleep, we calculated bedtime MSSD by computing the average of (night 2 bedtime – night 1 bedtime)^2, (night 3 bedtime – night 2 bedtime)^2, and (night 4 bedtime – night 3 bedtime)^2. Times are in units of hours. Compared to calculating standard deviation, this looks at change from day to day, not change relative to an overall mean\n\n\nTotalSleepTime\nAverage time in bed (the difference between wake time and bedtime) minus the length of total awake/restlessness in the main sleep episode, in minutes\n\n\nmidpoint_sleep\nAverage midpoint of bedtime and wake time, in minutes after 11 pm (for example, 364 is 5:04 am)\n\n\nfrac_nights_with_data\nFraction of nights with captured data for the subject. (Some students may not have worn their Fitbits every night, or the battery may have died.)\n\n\ndaytime_sleep\nAverage sleep time outside of the range of the main sleep episode, including short naps or sleep that occurred during the daytime, in minutes\n\n\ncum_gpa\nCumulative GPA (out of 4.0), for semesters before the one being studied. (Since these students are first-years during their spring semester, this is usually just their fall GPA. UW has quarters, so this includes both fall and winter quarters at UW.)\n\n\nterm_gpa\nEnd-of-term GPA (out of 4.0) for the semester being studied\n\n\nterm_units\nNumber of course units carried in the term\n\n\nZterm_units_ZofZ\nBecause each university counts units differently, each student’s units were Z-scored relative to the mean and standard deviation of all students in their study cohort. This score represented the student’s load relative to the average amount of units. The Z-scores for the cohorts were then combined and Z-scored again. 0 represents an average load, while positive values are above-average loads and negative values are below average."
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-    "text": "Questions\n\nThe original paper measured the diversity of each group using something called “group faultline analysis”, which looks at the group members’ genders, countries of origin, and ethnicities to calculate a diversity score. The calculation is somewhat involved, so we’ll make a simpler score. For each group, calculate the number of unique gender-ethnicity-country combinations (such as female-white-Russia or male-Indian-USA) among the group members, and store this with the other group information such as team size and performance. Also calculate the average testosterone level for each group.\nDo exploratory data analysis to explore the composition of groups, the typical amount of diversity, and the typical amounts of testosterone. Note particularly that the data includes the logs of the cortisol and testosterone levels as well as the raw levels; does your EDA suggest you should use the logs or the raw values?\nThe data also includes participant ages. It’s possible age is related to hormone levels, as is gender, and that both are related to final performance (perhaps older team members have more relevant experience, for example). Consider carefully whether you want to include these variables in the model, and how you should include them—average team member age? Gender proportion?\nBuild a model predicting group performance (final.performance) using the group’s diversity score (be sure to control for the size of the group) and its average testosterone level. Is there an interaction between the two? Do your results resemble those presented by the original study?\nThe dataset also includes cortisol measurements. Cortisol is a hormone with many effects and roles in the body, but is commonly known as a “stress hormone” because its levels often increase because of sleep deprivation, intense exercise, and stress. Curiously, though cortisol is included in the data, it is not mentioned in the paper apart from an appendix. Include it in your analysis. Do stressed groups have better or worse performance? Does stress change the effect of diversity?"
+    "text": "Questions\n\nCreate plots comparing the term GPAs by gender, race, and first generation status, which may all be related to academic success. Then, conduct one-way ANOVA analyses to see whether GPA differs across different groups.\nCreate a boxplot comparing the average total sleep time across schools.\nFind the average total sleep time for each school. Conduct a One-Way ANOVA analysis to see whether total sleep time differs across different schools.\nExplore the linear relationship between total sleep time and term GPA. Is the relationship significant, controlling for cumulative GPA?\nA student from CMU has 360 hours of total sleep time. What is his/her predicted GPA?"
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-    "text": "References\nM. Akinola, E. Page-Gould, P. H. Mehta, Z. Liu (2018). Hormone-Diversity Fit: Collective Testosterone Moderates the Effect of Diversity on Group Performance. Psychological Science 29 (6): 859-867. https://doi.org/10.1177/0956797617744282\nData available from https://osf.io/8eqtc/"
+    "text": "References\nCreswell, J. D., Tumminia, M. J., Price, S., Sefidgar, Y., Cohen, S., Ren, Y., Brown, J., Dey, A. K., Dutcher, J. M., Villalba, D., Mankoff, J., Xu, X., Creswell, K., Doryab, A., Mattingly, S., Striegel, A., Hachen, D., Martinez, G., & Lovett, M. C. (2023). Nightly sleep duration predicts grade point average in the first year of college. Proceedings of the National Academy of Sciences of the United States of America, 120(8), e2209123120. https://doi.org/10.1073/pnas.2209123120."
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-    "text": "Excess consumption of sugary drinks–soda, lemonade, sweet tea, and everything in between–has been blamed for a variety of health problems, like diabetes and obesity, which collectively cause billions of dollars of health costs each year. Some governments are taking action to reduce consumption of sugary drinks; most famously, the New York City Board of Health passed a regulation in 2012 banning the sale of sugary drinks in containers larger than 16 ounces. Naturally there was a backlash, and eventually a court case ruled the board didn’t have the authority to make such a rule.\nRegardless of New York’s legal troubles, and setting aside the question of whether sugary drinks are solely to blame for the health problems they contribute to, we can ask the question “What kinds of regulations or serving suggestions would make people drink smaller amounts?” Perhaps by limiting size, changing serving methods, or with other small changes we could encourage people to drink less without an outright ban.\nResearchers conducted a series of experiments to try different strategies. The dataset here is from one of those experiments.\nParticipants were invited to the lab for 90 minutes, during which they sat in a cubicle and completed surveys and tasks for other unrelated psychological studies. The experimenters provided them a bag of potato chips at the start to make them thirsty, then offered them iced tea or lemonade during the experiment; they were given a small amount of money at the start to use to buy the drinks, and paid per drink.\nThere were two different manipulations. Participants were randomly assigned to receive one of two sets of options:\n\nA medium drink in a 16 ounce cup, or a large drink in a 24 ounce cup (“typical” condition), or\nA medium drink in a 16 ounce cup, or a large drink served in two separate 12 ounce cups (the “bundled” condition).\n\nThere were also two options for service, randomly assigned. Either\n\nA research assistant walked to each cubicle, asking each participant what they’d like to drink (“waiter-served”), or\nParticipants walked to the back of the lab to buy their drinks (“self-served”).\n\nAfter the 90 minutes were up, the experimenters recorded how many ounces each participant drank (by weighing their cups), how many calories they drank, what kind of drinks they ordered, and a few demographic variables. Participants were also asked what their drink options were, to make sure they understood them."
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+    "text": "Makeup is often used by women, and indeed society often expects women to wear makeup whether they want to or not. Men, however, are not usually expected to wear makeup. Because it is unusual, researchers wanted to know if men who wear “subtle cosmetics” would be rated as more attractive by viewers.\nThe basic experiment was simple, as described in the abstract of the paper:\n\nTwenty men were photographed facing forward, under constant camera and lighting conditions, with neutral expressions, and closed mouths. Each man was photographed twice: once without any cosmetics applied and another time with subtle cosmetics applied by a professional makeup artist. Two hundred participants then rated those 40 images on attractiveness.\n\nEach of the 200 raters rated all 40 images, in random order, so they saw each male participant both with and without makeup. The twenty men were recruited from the area of Franklin and Marshall College through “word-of-mouth, social media, and paper flyers” and paid $10 for their time, while the raters were recruited through Amazon Mechanical Turk and paid $0.80 for their time."
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-    "text": "Excess consumption of sugary drinks–soda, lemonade, sweet tea, and everything in between–has been blamed for a variety of health problems, like diabetes and obesity, which collectively cause billions of dollars of health costs each year. Some governments are taking action to reduce consumption of sugary drinks; most famously, the New York City Board of Health passed a regulation in 2012 banning the sale of sugary drinks in containers larger than 16 ounces. Naturally there was a backlash, and eventually a court case ruled the board didn’t have the authority to make such a rule.\nRegardless of New York’s legal troubles, and setting aside the question of whether sugary drinks are solely to blame for the health problems they contribute to, we can ask the question “What kinds of regulations or serving suggestions would make people drink smaller amounts?” Perhaps by limiting size, changing serving methods, or with other small changes we could encourage people to drink less without an outright ban.\nResearchers conducted a series of experiments to try different strategies. The dataset here is from one of those experiments.\nParticipants were invited to the lab for 90 minutes, during which they sat in a cubicle and completed surveys and tasks for other unrelated psychological studies. The experimenters provided them a bag of potato chips at the start to make them thirsty, then offered them iced tea or lemonade during the experiment; they were given a small amount of money at the start to use to buy the drinks, and paid per drink.\nThere were two different manipulations. Participants were randomly assigned to receive one of two sets of options:\n\nA medium drink in a 16 ounce cup, or a large drink in a 24 ounce cup (“typical” condition), or\nA medium drink in a 16 ounce cup, or a large drink served in two separate 12 ounce cups (the “bundled” condition).\n\nThere were also two options for service, randomly assigned. Either\n\nA research assistant walked to each cubicle, asking each participant what they’d like to drink (“waiter-served”), or\nParticipants walked to the back of the lab to buy their drinks (“self-served”).\n\nAfter the 90 minutes were up, the experimenters recorded how many ounces each participant drank (by weighing their cups), how many calories they drank, what kind of drinks they ordered, and a few demographic variables. Participants were also asked what their drink options were, to make sure they understood them."
+    "text": "Makeup is often used by women, and indeed society often expects women to wear makeup whether they want to or not. Men, however, are not usually expected to wear makeup. Because it is unusual, researchers wanted to know if men who wear “subtle cosmetics” would be rated as more attractive by viewers.\nThe basic experiment was simple, as described in the abstract of the paper:\n\nTwenty men were photographed facing forward, under constant camera and lighting conditions, with neutral expressions, and closed mouths. Each man was photographed twice: once without any cosmetics applied and another time with subtle cosmetics applied by a professional makeup artist. Two hundred participants then rated those 40 images on attractiveness.\n\nEach of the 200 raters rated all 40 images, in random order, so they saw each male participant both with and without makeup. The twenty men were recruited from the area of Franklin and Marshall College through “word-of-mouth, social media, and paper flyers” and paid $10 for their time, while the raters were recruited through Amazon Mechanical Turk and paid $0.80 for their time."
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-    "text": "Data\nThere were 623 total participants in this study, of whom 362 ordered a drink. Each row represents one participant.\n\nData preview\n\n sugary-drinks.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nid\nUnique ID for each study participant\n\n\nIV_condition\nExperimental condition assigned. 1: waiter-served, typical. 2: waiter-served, bundled. 3: self-served, typical. 4: self-served, bundled\n\n\nIV_service_dummy\n0 for waiter-served, 1 for self-served\n\n\nIV_portion_dummy\n0 for typical drink portions, 1 for bundled drink portions\n\n\nIV_service_dummy_by_IV_portion_dummy\nInteraction of the two previous dummy variables (1 if both are 1).\n\n\nDV_order_at_all\n1 if the participant ordered any drinks at all\n\n\nDV_order_large\n1 if they ordered the large size, 0 if they ordered medium. Left blank if they didn’t order any drinks\n\n\nDV_ounces_consumed\nTotal number of ounces consumed by the participant\n\n\nDV_calories_consumed\nTotal number of calories consumed by the participant\n\n\nflavor\n0: Snapple lemon tea, 1: Vitamin Water squeezed lemonade. Left blank if the participant did not order a drink.\n\n\nsex\n1: male, 2: female\n\n\nage\nAge, in years\n\n\nethnicity\n1: African American, 2: American Indian or Alaskan Native, 3: Asian, 4: Caucasian, 5: Hispanic/Latino, 6: Pacific Islander, 7: Other or mixed\n\n\ncomprehension_check\n\n\n\nfailed_comprehension_check\nDid the participant fail a test at the end to make sure they knew what their drink options were? 0: no, 1: yes"
+    "text": "Data\nThe dataset is provided in two forms.\nThe first form presents the average rating for each face (of 20) with and without makeup, and can be used for simple analyses of the effect of makeup. The second form presents every rating as a row, producing 200 × 40 = 8,000 observations, each annotated with information about the participant and the rater.\n\nData preview\n\n male-makeup-faces.csv \n\n\n\n\n\n\n male-makeup-ratings.csv \n\n\n\n\n\n\n\nVariable descriptions\n\nmale-makeup-faces.csv\n\n\nVariable\nDescription\n\n\n\n\nFaceID\nNumber identifying the participant whose face was rated\n\n\nAge\nAge of the participant whose face was rated\n\n\nRace\nRace of the participant whose face was rated\n\n\nNoMakeup\nAverage rating, by the 200 raters, of the attractiveness of the participant’s face without makeup. The scale is from 1 to 7, where 1 = ‘Not at all attractive’ and 7 = ‘Very attractive’.\n\n\nMakeup\nAverage rating, by the 200 raters, of the attractiveness of the participant’s face with makeup. The scale is from 1 to 7, where 1 = ‘Not at all attractive’ and 7 = ‘Very attractive’.\n\n\n\n\nmale-makeup-ratings.csv\n\n\nVariable\nDescription\n\n\n\n\nRater_Sex\nSex of this rater\n\n\nRater_Age\nAge of this rater\n\n\nRater_Race\nRace of this rater\n\n\nRater\nNumber identifying the rater\n\n\nParticipant\nNumber identifying the participant whose face was rated\n\n\nMakeup\nMM if the participant wore makeup in the photo, NM otherwise\n\n\nrating\nRating of the attractiveness of the participant’s face. The scale is from 1 to 7, where 1 = ‘Not at all attractive’ and 7 = ‘Very attractive’.\n\n\nParticipant_Age\nAge of the participant whose face was rated\n\n\nParticipant_Race\nRace of the participant whose face was rated"
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-    "text": "Questions\n\nPerform basic EDA. What fraction of people ordered drinks, and of those who did, what fraction ordered medium or large? Which type of drink was most popular? Does there seem to be any difference depending on their drink options or service style?\nBuild a model to predict whether each participant orders a large drink, depending on the portion (typical vs. bundled) and the type of service (waiter-served or self service). Consider including an interaction if necessary.\nDid the portion type or service type affect drink orders? In what way?\nParticipants didn’t necessarily finish the drinks they ordered. Analyze the number of calories they consumed, depending on the portion type and type of service. Again, consider including an interaction if it seems necessary.\nThe researchers hypothesized that bundling may reduce consumption, since having drinks in two separate cups makes it easier to realize how much you’re drinking. Is there evidence to support this? Does it seem to matter whether participants had to give their order to an assistant or served themselves?\nRepeat your analysis, this time only including those who did not fail the comprehension check at the end, which checked that they correctly understood their drink options. Are the results consistent?\nThe experiment wasn’t intended for this purpose, but we can conduct a purely exploratory analysis using this data to explore drink preferences. Of those participants who ordered a drink, how many favored Snapple and how many favored lemonade? Do preferences depend on sex, age, or ethnicity? Build a model to predict the flavor of drink a participant ordered. How accurate is it?"
+    "text": "Questions\n\nConduct a t test or ANOVA analysis on the face dataset. Is there evidence that makeup increases the attractiveness of male faces? How large is the effect, if any?\nOf course, different raters may have different standards, tending to rate higher or lower than other raters. Using the full ratings data, build a model that allows ratings to depend both on the person being rated and on the rater, as well as on whether the participant was wearing makeup. Summarize your results.\nWe could think of raters as merely a sample from the population of possible raters who could rate these faces. Adapt your model into a hierarchical model and summarize your results."
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-    "text": "References\nLeslie K. John, Grant E. Donnelly, and Christina A. Roberto (2017). Psychologically Informed Implementations of Sugary-Drink Portion Limits. Psychological Science 28 (5) 620-629. https://doi.org/10.1177/0956797617692041\nData available from the Open Science Framework, at https://osf.io/93m8y"
+    "text": "References\nBatres C, Robinson H (2022). Makeup increases attractiveness in male faces. PLoS ONE 17(11): e0275662. https://doi.org/10.1371/journal.pone.0275662\nData available from the Open Science Framework: https://osf.io/j6zvy/"
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     "text": "References\nThe original experiment was: Gervais WM, Norenzayan A. (2012) Analytic Thinking Promotes Religious Disbelief. Science 336 (6080): 493–496. https://doi.org/10.1126/science.1215647\nThe replication was: Sanchez C, Sundermeier B, Gray K, Calin-Jageman RJ (2017) Direct replication of Gervais & Norenzayan (2012): No evidence that analytic thinking decreases religious belief. PLoS ONE 12(2): e0172636. https://doi.org/10.1371/journal.pone.0172636\nReplication data obtained from https://osf.io/qc6rh/ and subsequently converted and cleaned."
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-    "text": "Makeup is often used by women, and indeed society often expects women to wear makeup whether they want to or not. Men, however, are not usually expected to wear makeup. Because it is unusual, researchers wanted to know if men who wear “subtle cosmetics” would be rated as more attractive by viewers.\nThe basic experiment was simple, as described in the abstract of the paper:\n\nTwenty men were photographed facing forward, under constant camera and lighting conditions, with neutral expressions, and closed mouths. Each man was photographed twice: once without any cosmetics applied and another time with subtle cosmetics applied by a professional makeup artist. Two hundred participants then rated those 40 images on attractiveness.\n\nEach of the 200 raters rated all 40 images, in random order, so they saw each male participant both with and without makeup. The twenty men were recruited from the area of Franklin and Marshall College through “word-of-mouth, social media, and paper flyers” and paid $10 for their time, while the raters were recruited through Amazon Mechanical Turk and paid $0.80 for their time."
+    "text": "Excess consumption of sugary drinks–soda, lemonade, sweet tea, and everything in between–has been blamed for a variety of health problems, like diabetes and obesity, which collectively cause billions of dollars of health costs each year. Some governments are taking action to reduce consumption of sugary drinks; most famously, the New York City Board of Health passed a regulation in 2012 banning the sale of sugary drinks in containers larger than 16 ounces. Naturally there was a backlash, and eventually a court case ruled the board didn’t have the authority to make such a rule.\nRegardless of New York’s legal troubles, and setting aside the question of whether sugary drinks are solely to blame for the health problems they contribute to, we can ask the question “What kinds of regulations or serving suggestions would make people drink smaller amounts?” Perhaps by limiting size, changing serving methods, or with other small changes we could encourage people to drink less without an outright ban.\nResearchers conducted a series of experiments to try different strategies. The dataset here is from one of those experiments.\nParticipants were invited to the lab for 90 minutes, during which they sat in a cubicle and completed surveys and tasks for other unrelated psychological studies. The experimenters provided them a bag of potato chips at the start to make them thirsty, then offered them iced tea or lemonade during the experiment; they were given a small amount of money at the start to use to buy the drinks, and paid per drink.\nThere were two different manipulations. Participants were randomly assigned to receive one of two sets of options:\n\nA medium drink in a 16 ounce cup, or a large drink in a 24 ounce cup (“typical” condition), or\nA medium drink in a 16 ounce cup, or a large drink served in two separate 12 ounce cups (the “bundled” condition).\n\nThere were also two options for service, randomly assigned. Either\n\nA research assistant walked to each cubicle, asking each participant what they’d like to drink (“waiter-served”), or\nParticipants walked to the back of the lab to buy their drinks (“self-served”).\n\nAfter the 90 minutes were up, the experimenters recorded how many ounces each participant drank (by weighing their cups), how many calories they drank, what kind of drinks they ordered, and a few demographic variables. Participants were also asked what their drink options were, to make sure they understood them."
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-    "text": "Makeup is often used by women, and indeed society often expects women to wear makeup whether they want to or not. Men, however, are not usually expected to wear makeup. Because it is unusual, researchers wanted to know if men who wear “subtle cosmetics” would be rated as more attractive by viewers.\nThe basic experiment was simple, as described in the abstract of the paper:\n\nTwenty men were photographed facing forward, under constant camera and lighting conditions, with neutral expressions, and closed mouths. Each man was photographed twice: once without any cosmetics applied and another time with subtle cosmetics applied by a professional makeup artist. Two hundred participants then rated those 40 images on attractiveness.\n\nEach of the 200 raters rated all 40 images, in random order, so they saw each male participant both with and without makeup. The twenty men were recruited from the area of Franklin and Marshall College through “word-of-mouth, social media, and paper flyers” and paid $10 for their time, while the raters were recruited through Amazon Mechanical Turk and paid $0.80 for their time."
+    "text": "Excess consumption of sugary drinks–soda, lemonade, sweet tea, and everything in between–has been blamed for a variety of health problems, like diabetes and obesity, which collectively cause billions of dollars of health costs each year. Some governments are taking action to reduce consumption of sugary drinks; most famously, the New York City Board of Health passed a regulation in 2012 banning the sale of sugary drinks in containers larger than 16 ounces. Naturally there was a backlash, and eventually a court case ruled the board didn’t have the authority to make such a rule.\nRegardless of New York’s legal troubles, and setting aside the question of whether sugary drinks are solely to blame for the health problems they contribute to, we can ask the question “What kinds of regulations or serving suggestions would make people drink smaller amounts?” Perhaps by limiting size, changing serving methods, or with other small changes we could encourage people to drink less without an outright ban.\nResearchers conducted a series of experiments to try different strategies. The dataset here is from one of those experiments.\nParticipants were invited to the lab for 90 minutes, during which they sat in a cubicle and completed surveys and tasks for other unrelated psychological studies. The experimenters provided them a bag of potato chips at the start to make them thirsty, then offered them iced tea or lemonade during the experiment; they were given a small amount of money at the start to use to buy the drinks, and paid per drink.\nThere were two different manipulations. Participants were randomly assigned to receive one of two sets of options:\n\nA medium drink in a 16 ounce cup, or a large drink in a 24 ounce cup (“typical” condition), or\nA medium drink in a 16 ounce cup, or a large drink served in two separate 12 ounce cups (the “bundled” condition).\n\nThere were also two options for service, randomly assigned. Either\n\nA research assistant walked to each cubicle, asking each participant what they’d like to drink (“waiter-served”), or\nParticipants walked to the back of the lab to buy their drinks (“self-served”).\n\nAfter the 90 minutes were up, the experimenters recorded how many ounces each participant drank (by weighing their cups), how many calories they drank, what kind of drinks they ordered, and a few demographic variables. Participants were also asked what their drink options were, to make sure they understood them."
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-    "text": "Data\nThe dataset is provided in two forms.\nThe first form presents the average rating for each face (of 20) with and without makeup, and can be used for simple analyses of the effect of makeup. The second form presents every rating as a row, producing 200 × 40 = 8,000 observations, each annotated with information about the participant and the rater.\n\nData preview\n\n male-makeup-faces.csv \n\n\n\n\n\n\n male-makeup-ratings.csv \n\n\n\n\n\n\n\nVariable descriptions\n\nmale-makeup-faces.csv\n\n\nVariable\nDescription\n\n\n\n\nFaceID\nNumber identifying the participant whose face was rated\n\n\nAge\nAge of the participant whose face was rated\n\n\nRace\nRace of the participant whose face was rated\n\n\nNoMakeup\nAverage rating, by the 200 raters, of the attractiveness of the participant’s face without makeup. The scale is from 1 to 7, where 1 = ‘Not at all attractive’ and 7 = ‘Very attractive’.\n\n\nMakeup\nAverage rating, by the 200 raters, of the attractiveness of the participant’s face with makeup. The scale is from 1 to 7, where 1 = ‘Not at all attractive’ and 7 = ‘Very attractive’.\n\n\n\n\nmale-makeup-ratings.csv\n\n\nVariable\nDescription\n\n\n\n\nRater_Sex\nSex of this rater\n\n\nRater_Age\nAge of this rater\n\n\nRater_Race\nRace of this rater\n\n\nRater\nNumber identifying the rater\n\n\nParticipant\nNumber identifying the participant whose face was rated\n\n\nMakeup\nMM if the participant wore makeup in the photo, NM otherwise\n\n\nrating\nRating of the attractiveness of the participant’s face. The scale is from 1 to 7, where 1 = ‘Not at all attractive’ and 7 = ‘Very attractive’.\n\n\nParticipant_Age\nAge of the participant whose face was rated\n\n\nParticipant_Race\nRace of the participant whose face was rated"
+    "text": "Data\nThere were 623 total participants in this study, of whom 362 ordered a drink. Each row represents one participant.\n\nData preview\n\n sugary-drinks.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nid\nUnique ID for each study participant\n\n\nIV_condition\nExperimental condition assigned. 1: waiter-served, typical. 2: waiter-served, bundled. 3: self-served, typical. 4: self-served, bundled\n\n\nIV_service_dummy\n0 for waiter-served, 1 for self-served\n\n\nIV_portion_dummy\n0 for typical drink portions, 1 for bundled drink portions\n\n\nIV_service_dummy_by_IV_portion_dummy\nInteraction of the two previous dummy variables (1 if both are 1).\n\n\nDV_order_at_all\n1 if the participant ordered any drinks at all\n\n\nDV_order_large\n1 if they ordered the large size, 0 if they ordered medium. Left blank if they didn’t order any drinks\n\n\nDV_ounces_consumed\nTotal number of ounces consumed by the participant\n\n\nDV_calories_consumed\nTotal number of calories consumed by the participant\n\n\nflavor\n0: Snapple lemon tea, 1: Vitamin Water squeezed lemonade. Left blank if the participant did not order a drink.\n\n\nsex\n1: male, 2: female\n\n\nage\nAge, in years\n\n\nethnicity\n1: African American, 2: American Indian or Alaskan Native, 3: Asian, 4: Caucasian, 5: Hispanic/Latino, 6: Pacific Islander, 7: Other or mixed\n\n\ncomprehension_check\n\n\n\nfailed_comprehension_check\nDid the participant fail a test at the end to make sure they knew what their drink options were? 0: no, 1: yes"
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     "section": "Questions",
-    "text": "Questions\n\nConduct a t test or ANOVA analysis on the face dataset. Is there evidence that makeup increases the attractiveness of male faces? How large is the effect, if any?\nOf course, different raters may have different standards, tending to rate higher or lower than other raters. Using the full ratings data, build a model that allows ratings to depend both on the person being rated and on the rater, as well as on whether the participant was wearing makeup. Summarize your results.\nWe could think of raters as merely a sample from the population of possible raters who could rate these faces. Adapt your model into a hierarchical model and summarize your results."
+    "text": "Questions\n\nPerform basic EDA. What fraction of people ordered drinks, and of those who did, what fraction ordered medium or large? Which type of drink was most popular? Does there seem to be any difference depending on their drink options or service style?\nBuild a model to predict whether each participant orders a large drink, depending on the portion (typical vs. bundled) and the type of service (waiter-served or self service). Consider including an interaction if necessary.\nDid the portion type or service type affect drink orders? In what way?\nParticipants didn’t necessarily finish the drinks they ordered. Analyze the number of calories they consumed, depending on the portion type and type of service. Again, consider including an interaction if it seems necessary.\nThe researchers hypothesized that bundling may reduce consumption, since having drinks in two separate cups makes it easier to realize how much you’re drinking. Is there evidence to support this? Does it seem to matter whether participants had to give their order to an assistant or served themselves?\nRepeat your analysis, this time only including those who did not fail the comprehension check at the end, which checked that they correctly understood their drink options. Are the results consistent?\nThe experiment wasn’t intended for this purpose, but we can conduct a purely exploratory analysis using this data to explore drink preferences. Of those participants who ordered a drink, how many favored Snapple and how many favored lemonade? Do preferences depend on sex, age, or ethnicity? Build a model to predict the flavor of drink a participant ordered. How accurate is it?"
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     "section": "References",
-    "text": "References\nBatres C, Robinson H (2022). Makeup increases attractiveness in male faces. PLoS ONE 17(11): e0275662. https://doi.org/10.1371/journal.pone.0275662\nData available from the Open Science Framework: https://osf.io/j6zvy/"
+    "text": "References\nLeslie K. John, Grant E. Donnelly, and Christina A. Roberto (2017). Psychologically Informed Implementations of Sugary-Drink Portion Limits. Psychological Science 28 (5) 620-629. https://doi.org/10.1177/0956797617692041\nData available from the Open Science Framework, at https://osf.io/93m8y"
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-    "text": "For many reasons, it’s important for students to succeed during their first year of college. Success during the first year affects whether students will stay to complete the rest of their degree, and college GPA seems to predict future success in jobs. Universities often invest large amounts of time and money to help students adapt to college during their first year, through special programs, tutoring, orientation sessions, and so on.\nBesides programs offered by universities, we can also ask what students can do to improve their chances. One potential factor is sleep. College students often have poor sleep habits, staying up late and sleeping short hours, and a great deal of research suggests that lack of sleep can harm cognitive performance. It seems reasonable, then, to study how sleep habits in college students are related to their grade point averages (GPAs).\nTo assess this, this study recruited first-year students at three universities:\n\nCarnegie Mellon University (CMU), a STEM-focused private university\nThe University of Washington (UW), a large public university\nNotre Dame University (ND), a private Catholic university\n\nEach student participant was given a device to track their sleep patterns for a month early in their spring academic term. The researchers then obtained grade data from the university registrars, along with demographic information about the students. The demographic information included other factors that might be related to the students’ GPAs, such as whether they were first-generation students whose parents never attended college, so these factors could be controlled for."
+    "text": "Some research has suggested that groups of people working on a task can do better if the group is more diverse, since diverse group members can suggest more creative ideas and make better decisions. At the same time, diverse groups can have more conflict than less diverse groups, possibly eliminating those benefits.\nThis dataset is from a study that attempted to understand these factors and how they relate to the testosterone levels of members of the group. People with high levels of testosterone tend to be competitive and may try to dominate their groups, leading to conflict, while people with low testosterone may be more cooperative. So do groups with low testosterone work better together, and benefit more from diversity, than those with high testosterone?\nThe study involved 370 students working on their Master of Business Administration degrees at Columbia Business School. The students were randomly assigned to groups of three to six people, and their testosterone levels were measured. The groups then did a week-long project where they pretended to run blood-testing laboratories, with the goal of making their laboratories perform better than those run by other groups in the class. Group members had to collaborate with each other to decide on their strategy. At the end of the project, the researchers recorded how well each group’s laboratories did, how much money they made, and so on.\nThe result, according to the study, was that\n\nThe findings suggest that diversity is beneficial for performance, but only if group-level testosterone is low; diversity has a negative effect on performance if group-level testosterone is high."
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-    "text": "For many reasons, it’s important for students to succeed during their first year of college. Success during the first year affects whether students will stay to complete the rest of their degree, and college GPA seems to predict future success in jobs. Universities often invest large amounts of time and money to help students adapt to college during their first year, through special programs, tutoring, orientation sessions, and so on.\nBesides programs offered by universities, we can also ask what students can do to improve their chances. One potential factor is sleep. College students often have poor sleep habits, staying up late and sleeping short hours, and a great deal of research suggests that lack of sleep can harm cognitive performance. It seems reasonable, then, to study how sleep habits in college students are related to their grade point averages (GPAs).\nTo assess this, this study recruited first-year students at three universities:\n\nCarnegie Mellon University (CMU), a STEM-focused private university\nThe University of Washington (UW), a large public university\nNotre Dame University (ND), a private Catholic university\n\nEach student participant was given a device to track their sleep patterns for a month early in their spring academic term. The researchers then obtained grade data from the university registrars, along with demographic information about the students. The demographic information included other factors that might be related to the students’ GPAs, such as whether they were first-generation students whose parents never attended college, so these factors could be controlled for."
+    "text": "Some research has suggested that groups of people working on a task can do better if the group is more diverse, since diverse group members can suggest more creative ideas and make better decisions. At the same time, diverse groups can have more conflict than less diverse groups, possibly eliminating those benefits.\nThis dataset is from a study that attempted to understand these factors and how they relate to the testosterone levels of members of the group. People with high levels of testosterone tend to be competitive and may try to dominate their groups, leading to conflict, while people with low testosterone may be more cooperative. So do groups with low testosterone work better together, and benefit more from diversity, than those with high testosterone?\nThe study involved 370 students working on their Master of Business Administration degrees at Columbia Business School. The students were randomly assigned to groups of three to six people, and their testosterone levels were measured. The groups then did a week-long project where they pretended to run blood-testing laboratories, with the goal of making their laboratories perform better than those run by other groups in the class. Group members had to collaborate with each other to decide on their strategy. At the end of the project, the researchers recorded how well each group’s laboratories did, how much money they made, and so on.\nThe result, according to the study, was that\n\nThe findings suggest that diversity is beneficial for performance, but only if group-level testosterone is low; diversity has a negative effect on performance if group-level testosterone is high."
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-    "text": "Data\nThere were a total of 634 participants in this study, who each received a Fitbit to track their sleep and physical activity. Students’ GPAs were collected from the university registrar. Each data row represents one participant.\nUsing the Fitbit data, the researchers extracted sleep “episodes”, which are simply time periods where the Fitbit reported the participant as sleeping. They only counted episodes at least 20 minutes long, separated by at least 5 minutes from other episodes. Gaps smaller than 5 minutes were ignored, so if a student slept for several hours, woke for a few minutes, and then slept again, this was counted as a single episode.\nThe longest sleep episode between noon of one day and noon of the next was considered the “main” episode; anything else was considered “daytime sleep”. For example, if a student slept from 4pm to 5pm, then again from 2am to 8am, the 2-8am window was the “main” episode (6 hours long), and the 4-5pm window was counted as 1 hour of daytime sleep. The beginning of the main episode was counted as the student’s “bedtime”, so in this example, the student’s bedtime was 2am.\nThe data is divided into five separate studies, by university and semester:\n\n\n\nStudy\nUniversity\nSemester\n\n\n\n\n1\nCarnegie Mellon University\nSpring 2018\n\n\n2\nUniversity of Washington\nSpring 2018\n\n\n3\nUniversity of Washington\nSpring 2019\n\n\n4\nNotre Dame University\nSpring 2016\n\n\n5\nCarnegie Mellon University\nSpring 2017\n\n\n\n\nData preview\n\n cmu-sleep.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nsubject_id\nUnique ID of the subject\n\n\nstudy\nStudy number, as shown in the table above\n\n\ncohort\nCodename of the cohort that the subject belongs to\n\n\ndemo_race\nBinary label for underrepresented and non-underrepresented students (underrepresented = 0, non-underpresented = 1). Students were considered underrepresented if either parent was Black, Hispanic or Latino, Native American, or Pacific Islander. Students were non-underrepresented if neither parent was from an underrepresented category (i.e., both parents had White or Asian ancestry).\n\n\ndemo_gender\nGender of the subject (male = 0, female = 1), as reported by their institution.\n\n\ndemo_firstgen\nFirst-generation status (non-first gen = 0, first-gen = 1). Students were considered first-generation if neither parent completed any college (i.e., high school diploma or less)\n\n\nbedtime_mssd\nMean successive squared difference of bedtime. This measures bedtime variability, and is calculated as the average of the squared difference of bedtime on consecutive nights. For example, if we had four successive nights of sleep, we calculated bedtime MSSD by computing the average of (night 2 bedtime – night 1 bedtime)^2, (night 3 bedtime – night 2 bedtime)^2, and (night 4 bedtime – night 3 bedtime)^2. Times are in units of hours. Compared to calculating standard deviation, this looks at change from day to day, not change relative to an overall mean\n\n\nTotalSleepTime\nAverage time in bed (the difference between wake time and bedtime) minus the length of total awake/restlessness in the main sleep episode, in minutes\n\n\nmidpoint_sleep\nAverage midpoint of bedtime and wake time, in minutes after 11 pm (for example, 364 is 5:04 am)\n\n\nfrac_nights_with_data\nFraction of nights with captured data for the subject. (Some students may not have worn their Fitbits every night, or the battery may have died.)\n\n\ndaytime_sleep\nAverage sleep time outside of the range of the main sleep episode, including short naps or sleep that occurred during the daytime, in minutes\n\n\ncum_gpa\nCumulative GPA (out of 4.0), for semesters before the one being studied. (Since these students are first-years during their spring semester, this is usually just their fall GPA. UW has quarters, so this includes both fall and winter quarters at UW.)\n\n\nterm_gpa\nEnd-of-term GPA (out of 4.0) for the semester being studied\n\n\nterm_units\nNumber of course units carried in the term\n\n\nZterm_units_ZofZ\nBecause each university counts units differently, each student’s units were Z-scored relative to the mean and standard deviation of all students in their study cohort. This score represented the student’s load relative to the average amount of units. The Z-scores for the cohorts were then combined and Z-scored again. 0 represents an average load, while positive values are above-average loads and negative values are below average."
+    "text": "Data\nThe first data file contains individual data: the age, gender, and ethnicity of each student, along with their testosterone and cortisol levels. The second file reports the performance of the teams on their projects. There were 370 students in 74 groups.\n\nData preview\n\n hormone-diversity-individual.csv \n\n\n\n\n\n\n hormone-diversity-teams.csv \n\n\n\n\n\n\n\nVariable descriptions\n\nhormone-diversity-individual.csv\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nID\nParticipant ID number\n\n\nteam.id\nID number of the team this participant belonged to\n\n\nAge\nAge, in years\n\n\nGender\nGender (Male or Female)\n\n\nEthnicity\nEthnicity of the participant\n\n\nCortisol\nParticipant’s cortisol levels, nMol/L\n\n\nTestosterone\nParticipant’s testosterone levels, pg/mL\n\n\nlog.cortisol\nNatural logarithm of the participant’s cortisol level\n\n\nlog.testosterone\nNatural logarithm of the participant’s testosterone level\n\n\nCountry\nCountry of citizenship of the participant\n\n\n\n\nhormone-diversity-teams.csv\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nteam.id\nTeam ID number\n\n\nteam.size\nNumber of people on the team\n\n\nfinal.performance\nThe team’s final performance score\n\n\ntime.of.day\nThe time of day the team’s hormone sample was collected (hh.mm)\n\n\nfemales\nNumber of females in the group\n\n\nfinal.cash\nTotal cash earned by the team\n\n\nfinal.contracts\nTotal number of contracts won by the team\n\n\nfinal.reorders\nTotal number of reorders won by the team\n\n\nfinal.rank\nTeam’s final rank at the end of the project, relative to other teams in their class section\n\n\ninterim.performance\nSame as above, but measured at Day 5 of the study (missing for some teams)\n\n\ninterim.cash\n\n\n\ninterim.contracts\n\n\n\ninterim.reorders\n\n\n\ninterim.rank"
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-    "text": "Questions\n\nCreate plots comparing the term GPAs by gender, race, and first generation status, which may all be related to academic success. Then, conduct one-way ANOVA analyses to see whether GPA differs across different groups.\nCreate a boxplot comparing the average total sleep time across schools.\nFind the average total sleep time for each school. Conduct a One-Way ANOVA analysis to see whether total sleep time differs across different schools.\nExplore the linear relationship between total sleep time and term GPA. Is the relationship significant, controlling for cumulative GPA?\nA student from CMU has 360 hours of total sleep time. What is his/her predicted GPA?"
+    "text": "Questions\n\nThe original paper measured the diversity of each group using something called “group faultline analysis”, which looks at the group members’ genders, countries of origin, and ethnicities to calculate a diversity score. The calculation is somewhat involved, so we’ll make a simpler score. For each group, calculate the number of unique gender-ethnicity-country combinations (such as female-white-Russia or male-Indian-USA) among the group members, and store this with the other group information such as team size and performance. Also calculate the average testosterone level for each group.\nDo exploratory data analysis to explore the composition of groups, the typical amount of diversity, and the typical amounts of testosterone. Note particularly that the data includes the logs of the cortisol and testosterone levels as well as the raw levels; does your EDA suggest you should use the logs or the raw values?\nThe data also includes participant ages. It’s possible age is related to hormone levels, as is gender, and that both are related to final performance (perhaps older team members have more relevant experience, for example). Consider carefully whether you want to include these variables in the model, and how you should include them—average team member age? Gender proportion?\nBuild a model predicting group performance (final.performance) using the group’s diversity score (be sure to control for the size of the group) and its average testosterone level. Is there an interaction between the two? Do your results resemble those presented by the original study?\nThe dataset also includes cortisol measurements. Cortisol is a hormone with many effects and roles in the body, but is commonly known as a “stress hormone” because its levels often increase because of sleep deprivation, intense exercise, and stress. Curiously, though cortisol is included in the data, it is not mentioned in the paper apart from an appendix. Include it in your analysis. Do stressed groups have better or worse performance? Does stress change the effect of diversity?"
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     "section": "References",
-    "text": "References\nCreswell, J. D., Tumminia, M. J., Price, S., Sefidgar, Y., Cohen, S., Ren, Y., Brown, J., Dey, A. K., Dutcher, J. M., Villalba, D., Mankoff, J., Xu, X., Creswell, K., Doryab, A., Mattingly, S., Striegel, A., Hachen, D., Martinez, G., & Lovett, M. C. (2023). Nightly sleep duration predicts grade point average in the first year of college. Proceedings of the National Academy of Sciences of the United States of America, 120(8), e2209123120. https://doi.org/10.1073/pnas.2209123120."
+    "text": "References\nM. Akinola, E. Page-Gould, P. H. Mehta, Z. Liu (2018). Hormone-Diversity Fit: Collective Testosterone Moderates the Effect of Diversity on Group Performance. Psychological Science 29 (6): 859-867. https://doi.org/10.1177/0956797617744282\nData available from https://osf.io/8eqtc/"
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-    "text": "Estimating the properties of galaxies, and even where they are, is a challenging process. The Rubin Observatory, a sky survey telescope located in Chile, will once it becomes operational image tens of billions of astronomical objects, the vast majority of which will be galaxies that have never been imaged before. Analyses of these data will require sophisticated methodologies, ones that will allow us to first determine where the galaxies are (i.e., how far away they are), and then conditional on the distance, how massive they are. Given galaxy distance and mass data, we can test theories of how the Universe evolves, by comparing simulated galaxy data with these data.\nThe Buzzard-V1.0 simulation was used to generate a realistic sample of Rubin Observatory data. In this dataset are measurements for 111,172 galaxies. Developers used these data to benchmark, e.g., methods for estimating galaxy distance. Here, we can assume the distance has been estimated well, and use these data to try to model galaxy mass as a function of brightness and distance."
+    "text": "Psychologists and food scientists have, in recent years, produced a great deal of research on a simple question: Do people eat more food if their meals are served on larger plates?\nThis question is of real practical importance. The ways food is packaged and presented undoubtedly affect how we eat it, and with obesity rates high across the Western world, simple ways to reduce overconsumption of food could have major public health benefits. Perhaps people overconsume food because they tend to eat most of what’s on their plates, leading them to eat more when the plate is large; if so, general advice to buy smaller plates could have a nontrivial effect.\nSeveral experiments suggested this hypothesis is true, and that reducing plate size would help. But some of these experiments were conducted by Cornell University’s Food and Brand Lab, and starting in 2017 researchers started noticing serious problems with the lab’s publications, including miscalculated test statistics, inconsistencies in results, and incorrect descriptions of experimental procedures; since then, many of the lab’s papers have been retracted entirely, and the lab’s director, Brian Wansink, resigned from Cornell. It is not clear which conclusions can be trusted and which cannot.\nThis study sought to settle the question by conducting a large, carefully designed experiment, whose design and analysis was preregistered so it could not be manipulated once data was collected. In the study, participants were recruited and told they were participating in an experiment “examining the impact of time of the day on a range of mental processes,” and that they happened to be scheduled for a lunchtime session. They were told not to eat anything for three hours before the session; upon arriving, they completed various survey tasks, then were invited to serve themselves from a food trolley. The size of plate they were provided (small or large) was randomly assigned, and the exact quantity of food they consumed was measured (by measuring what was left in the trolley and on their plate). The session was also video recorded by hidden cameras, so researchers could count how many bites the participants took and the rate at which they ate."
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-    "text": "Estimating the properties of galaxies, and even where they are, is a challenging process. The Rubin Observatory, a sky survey telescope located in Chile, will once it becomes operational image tens of billions of astronomical objects, the vast majority of which will be galaxies that have never been imaged before. Analyses of these data will require sophisticated methodologies, ones that will allow us to first determine where the galaxies are (i.e., how far away they are), and then conditional on the distance, how massive they are. Given galaxy distance and mass data, we can test theories of how the Universe evolves, by comparing simulated galaxy data with these data.\nThe Buzzard-V1.0 simulation was used to generate a realistic sample of Rubin Observatory data. In this dataset are measurements for 111,172 galaxies. Developers used these data to benchmark, e.g., methods for estimating galaxy distance. Here, we can assume the distance has been estimated well, and use these data to try to model galaxy mass as a function of brightness and distance."
+    "text": "Psychologists and food scientists have, in recent years, produced a great deal of research on a simple question: Do people eat more food if their meals are served on larger plates?\nThis question is of real practical importance. The ways food is packaged and presented undoubtedly affect how we eat it, and with obesity rates high across the Western world, simple ways to reduce overconsumption of food could have major public health benefits. Perhaps people overconsume food because they tend to eat most of what’s on their plates, leading them to eat more when the plate is large; if so, general advice to buy smaller plates could have a nontrivial effect.\nSeveral experiments suggested this hypothesis is true, and that reducing plate size would help. But some of these experiments were conducted by Cornell University’s Food and Brand Lab, and starting in 2017 researchers started noticing serious problems with the lab’s publications, including miscalculated test statistics, inconsistencies in results, and incorrect descriptions of experimental procedures; since then, many of the lab’s papers have been retracted entirely, and the lab’s director, Brian Wansink, resigned from Cornell. It is not clear which conclusions can be trusted and which cannot.\nThis study sought to settle the question by conducting a large, carefully designed experiment, whose design and analysis was preregistered so it could not be manipulated once data was collected. In the study, participants were recruited and told they were participating in an experiment “examining the impact of time of the day on a range of mental processes,” and that they happened to be scheduled for a lunchtime session. They were told not to eat anything for three hours before the session; upon arriving, they completed various survey tasks, then were invited to serve themselves from a food trolley. The size of plate they were provided (small or large) was randomly assigned, and the exact quantity of food they consumed was measured (by measuring what was left in the trolley and on their plate). The session was also video recorded by hidden cameras, so researchers could count how many bites the participants took and the rate at which they ate."
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     "section": "Data",
-    "text": "Data\nThe dataset contains measures of magnitude and magnitude uncertainty in six astronomical bands (u for ultraviolet, g for green, r for red, i for infrared, and z and y for two additional infrared bands). Magnitude is a logarithmic measure of brightness, with an increase of 5 representing a decrease in brightness by a factor of 100, and with a value of zero being represented (roughly) by how the star Vega appears in the night sky. In addition, there is a redshift measured for each galaxy; it represents by how much light from the galaxy is stretched (by the expansion of Universe) as it travels to us. Thus higher redshifts represent larger distances. The last measurement is log.mass, which is the base-10 logarithm of the galaxy stellar mass in units of solar mass; for instance, log.mass = 10 means that the galaxy has a mass 10 billion times that of the Sun.\n\nData preview\n\n Buzzard_DC1.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\nVariable\nDescription\n\n\n\n\nu\nGalaxy magnitude in Rubin u band (320.5-393.5 nm)\n\n\ng\nGalaxy magnitude in Rubin g band (401.5-551.9 nm)\n\n\nr\nGalaxy magnitude in Rubin r band (552.0-691.0 nm)\n\n\ni\nGalaxy magnitude in Rubin i band (691.0-818.0 nm)\n\n\nz\nGalaxy magnitude in Rubin z band (818.0-923.5 nm)\n\n\ny\nGalaxy magnitude in Rubin y band (923.8-1084.5 nm)\n\n\nu.err\nUncertainty for u-band magnitude\n\n\ng.err\nUncertainty for g-band magnitude\n\n\nr.err\nUncertainty for r-band magnitude\n\n\ni.err\nUncertainty for i-band magnitude\n\n\nz.err\nUncertainty for z-band magnitude\n\n\ny.err\nUncertainty for y-band magnitude\n\n\nlog.mass\nGalaxy stellar mass (log-base-10 solar masses)\n\n\nredshift\nGalaxy redshift"
+    "text": "Data\nEach row of the dataset is one participant in the study. Beyond quantities of food consumed and the rate at which it was consumed, some basic demographic information was collected, as well as several standard psychological tasks.\nNote: A value of -999 means that value was not recorded; a value of -888 on a survey question means the participant answered that they would “rather not say.”\n\nData preview\n\n plate-size.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nDate\nDate this participant participated\n\n\nID\nParticipant ID number\n\n\nplate.size\n0 = small plate, 1 = large plate\n\n\nkcal.consumed\nTotal number of calories consumed\n\n\ng.consumed\nMass of food the participant ate (grams)\n\n\ng.served\nMass of food the participant put on their plate (grams)\n\n\ng.remaining\nMass of food left on participant’s plate (grams)\n\n\nservings\nNumber of times the participant served themself food\n\n\nmeal.duration\nTime from the first bite to the last bite (minutes)\n\n\nn.bites\nNumber of bites taken\n\n\nbite.size\nAverage bite size (total food mass / number of bites, grams)\n\n\neat.rate\nTime taken per bite (meal duration / number of bites)\n\n\nbite.rate\nNumber of bites per minute\n\n\nhighest.qual\nHighest educational qualification. 1 = none, 2 = up to 4 GCSEs, 3 = 5 or more GCSEs or 1 A-level, 4 = 2 or more A-levels, 5 = bachelor’s degree, 6 = post-graduate degree, 7 = rather not say.\n\n\nhighest.qual.split\nHighest educational qualification discretized\n\n\nincome\nTotal household income per year, before tax (self-reported). 1 = up to £4,499; 2 = up to £6,499; 3 = up to £7,499; 4 = up to £9,499; 5 = up to £11,499; 6 = up to £13,499; 7 = up to £15,499; 8 = up to £17,499; 9 = up to £24,999; 10 = up to £29,999; 11 = up to £39,999; 12 = up to £49,999; 13 = up to £74,999; 14 = up to £99,999; 15 = over £100,000\n\n\nincome.med.split\n0 = total household income below median; 1 = total household income above median.\n\n\nimd\nIndex of Multiple Deprivation of the area where the participant lives. Measures income, unemployment, education, crime, and other factors related to socioeconomic status and the living environment. Small values mean greater deprivation.\n\n\nimd.med.split\n0 = IMD below median value; 1 = IMD above median value.\n\n\nssrt\nStop Signal Task response time (milliseconds). A task intended to measure participants’ response inhibition and impulsivity; slow response times indicate poor impulse control.\n\n\nBIS-TOTAL\nTotal score from the Barratt Impulsiveness Scale. Higher scores indicate greater impulsiveness.\n\n\nBIS-Q1\nBIS item 1: ‘I plan tasks carefully.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q2\nBIS item 2: ‘I do things without thinking.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q3\nBIS item 3: ‘I make-up my mind quickly.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q4\nBIS item 4: ‘I am happy-go-lucky.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q5\nBIS item 5: ‘I don’t pay attention.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q6\nBIS item 6: ‘I have racing thoughts.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q7\nBIS item 7: ‘I plan trips well ahead of time.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q8\nBIS item 8: ‘I am self controlled.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q9\nBIS item 9: ‘I concentrate easily.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q10\nBIS item 10: ‘I save regularly.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q11\nBIS item 11: ‘I squirm at plays or lectures.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q12\nBIS item 12: ‘I am a careful thinker.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q13\nBIS item 13: ‘I plan for job security.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q14\nBIS item 14: ‘I say things without thinking.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q15\nBIS item 15: ‘I like to think about complex problems.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q16\nBIS item 16: ‘I change jobs.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q17\nBIS item 17: ‘I act on impulse.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q18\nBIS item 18: ‘I get easily bored when solving thought problems.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q19\nBIS item 19: ‘I act on the spur of the moment.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q20\nBIS item 20: ‘I am a steady thinker.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q21\nBIS item 21: ‘I change residences.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q22\nBIS item 22: ‘I buy things on impulse.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q23\nBIS item 23: ‘I can only think about one thing at a time.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q24\nBIS item 24: ‘I change hobbies.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q25\nBIS item 25: ‘I spend or charge more than I earn.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q26\nBIS item 26: ‘I often have extraneous thoughts when thinking.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q27\nBIS item 27: ‘I am more interested in the present than the future.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q28\nBIS item 28: ‘I am restless at the theater or lectures.’ 1 = rarely/never; 2 = occasionally; 3 = often; 4 = almost always/always.\n\n\nBIS-Q29\nBIS item 29: ‘I like puzzles.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nBIS-Q30\nBIS item 30: ‘I am future oriented.’ 1 = almost always/always; 2 = often; 3 = occasionally; 4 = rarely/never.\n\n\nline.task.correct\nNumber of correct answers to a line orientation task, out of 24 possible. Measures ability to orient objects in space.\n\n\ngender\nGender. 1 = male, 2 = female, 3 = other, 4 = rather not say.\n\n\nage\nAge (years)\n\n\nethnicity\nEthnicity. 1 = white; 2 = mixed white and black, Asian, Caribbean, or other; 3 = Asian; 4 = Black; 5 = other; 6 = rather not say.\n\n\nethnicity.dicot\nEthnicity dicotomized. 0 = white, 1 = non-white.\n\n\nBMI\nBody mass index\n\n\nhunger\nHow hungry the participant reported being before lunch. 100 point scale. 0 = not at all, 100 = very much.\n\n\nfullness\nHow full the participant reported being before lunch. 100 point scale.\n\n\nlike.food\nHow much the participant reported liking the food provided in the study. 100 point scale.\n\n\nfasting\nWhether the participant fasted for 3 hours before the study, as requested (yes/no)."
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     "section": "Questions",
-    "text": "Questions\nAs noted above, the idea here is to learn a statistical association between measures of magnitude and distance, and galaxy mass.\nOne wrinkle here that analysts can exploit is that the data contain standard error estimates for the magnitudes (though not for redshift, for which, in practice, the error would be \\(\\sim 10^{-5}\\))."
+    "text": "Questions\n\nThe simplest question is also the one that directly answers the study’s main question: Did participants given a larger plate eat more, on average, than those given a smaller plate?\nPerhaps the effect of plate size varies depending on impulsivity (measured by the Barratt Impulsiveness Scale and the Stop Signal Task) or socioeconomic status (measured by income, education, and the Index of Multiple Deprivation). One could imagine, for example, that people who regularly struggle to get enough food might be more affected by the desire to eat everything on their plate. Conduct an analysis to see if the effect of plate size differs depending on these factors.\nThe study was designed to have the sample size needed to get 80% statistical power to detect a difference of d = 0.5 standard deviations in the amount of food consumed between the two groups. The power analysis did not determine the sample size needed to detect if the effect of plate size differs depending on other factors. Use simulation to determine the power of the study to detect such differences. Is it capable of reliably detecting them?"
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+    "title": "Serving sizes and plate sizes",
     "section": "References",
-    "text": "References\nSchmidt, Malz, Soo, Almosallam, Brescia, Cavuoti, Cohen-Tanugi, Connolly, DeRose, Freeman, Graham, Iyer, Jarvis, Kalmbach, Kovacs, Lee, Longo, Morrison, Newman, Nourbakhsh, Nuss, Pospisil, Tranin, Wechsler, Zhou, Izbicki, (The LSST Dark Energy Science Collaboration). “Evaluation of probabilistic photometric redshift estimation approaches for The Rubin Observatory Legacy Survey of Space and Time (LSST)”. Monthly Notices of the Royal Astronomical Society 499, December 2020, pages 1587–1606. https://doi.org/10.1093/mnras/staa2799"
+    "text": "References\nDaina Kosīte, Laura M. König, Katie De-loyde, Ilse Lee, Emily Pechey, Natasha Clarke, Olivia Maynard, Richard W. Morris, Marcus R. Munafò, Theresa M. Marteau, Paul C. Fletcher & Gareth J. Hollands (2019). “Plate size and food consumption: a pre-registered experimental study in a general population sample,” International Journal of Behavioral Nutrition and Physical Activity 16 no. 75. https://doi.org/10.1186/s12966-019-0826-1\nData available from the Open Science Framework: https://osf.io/k74cu/"
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-    "href": "submit-data.html",
-    "title": "Submit a Dataset",
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+    "href": "external-sources.html",
+    "title": "External Sources",
     "section": "",
-    "text": "The simplest way to submit data is to use our online submission form. Provide the requested information and hit Submit; we’ll review the submission and eventually add it to the site, if it’s suitable.\nOur overall goal is to provide a collection of useful, real-world datasets from a variety of application areas that can be used as in-class examples, homework assignments, or course projects. The repository can always use new datasets, so we welcome submissions. Datasets should meet a few requirements:\nIf you’re familiar with R Markdown or Quarto, you can instead prepare the data description page yourself from a template. Read on for detailed instructions."
+    "text": "Here are some good outside sources for finding datasets suitable for classes:\n\nData is Plural, a weekly newsletter of interesting public datasets.\nSCORE Sports Data Repository, a collection of sports datasets for classroom use\nStanford Large Network Dataset Collection\nRoyal Society Open Science, an open-access general science journal whose papers have Data Availability statements and often have public data\nScientific Data, a journal publishing descriptions of open datasets (often large and with unusual structure)\nData in Brief, another journal publishing descriptions of open datasets\nOpen Case Studies, a collection of data analysis case studies showing analyses from conception to conclusion\nUCI Machine Learning Repository, containing hundreds of datasets suitable for machine learning tasks\nTidyTuesday, a project collecting datasets for weekly data wrangling and visualization events"
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-    "section": "Getting Started with Quarto",
-    "text": "Getting Started with Quarto\nThis website is built using the Quarto system and automatically rendered into a website. Data description pages can be made directly from a template file we provide.\nYou can get the template in two ways:\n\nCopy the _dataset-template.qmd file from our GitHub repository and save it on your computer. Once you’re done, you can email us the file and the data.\nFork our GitHub repository into your own GitHub account and edit it like any other Git repository. Once you’re done, you can submit a pull request."
+    "objectID": "index.html",
+    "href": "index.html",
+    "title": "CMU S&DS Data Repository",
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+    "text": "The Data Repository curates interesting datasets for use in statistics and data science education. Each dataset is supported by a story describing its origin and application, and a set of interesting questions that can be answered using the data. This means:\n\nEvery dataset has context in a scientific field, pop culture, or daily life.\nBeyond context, datasets are interesting. They feature more than just a dozen observations from an antiquated scientific study — many feature thousands of observations of dozens of variables, and answer questions interesting to a wide audience.\nJust like in science, some datasets give null results.\nInstructors can easily build lessons and assignments from the suggested questions.\n\nDatasets are organized by broad subject areas on the left, or you can browse a sortable list of all datasets."
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-    "section": "The Dataset Template",
-    "text": "The Dataset Template\nThe dataset template is a Quarto file; Quarto is much like R Markdown and is supported by recent versions of RStudio. Quarto files can contain R code, just like R Markdown, so your data description can include graphics and tables generated by R code embedded in the file, if you think this would be helpful to illustrate important features of the data. As you fill out the template, you can use RStudio’s “Render” button to see a preview of the finished page.\nThe template asks for:\n\nBasic metadata, such as the statistical methods applicable to the data, a short title, and your name. (All submissions are credited with the name of the submitter.)\nA description of the problem. This should be sufficient to motivate the analysis and help students understand the setting.\nA description of the data and the variables. It should be clear what each row of data represents and what all variables mean. Units of measure should be included whenever possible.\nReferences to the original source. If the source is an academic paper and the data is deposited in a third-party repository (such as Zenodo, Figshare, or Dryad), include references to both the paper and the archived dataset. When available, include the DOIs of the references.\n\nThe template is meant to prevent a common problem with course datasets: they get passed down from instructor to instructor, and eventually all information about the original source is lost. The dataset may be presented to students without context or important details (like units), and the instructor may not be able to find the original data or references to answer questions about the data. By providing sufficient detail, we can make datasets reusable for years to come."
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+    "text": "You’ve probably seen that insects seem to be attracted to artificial lights at night. Insects appear to be most attractive to blue and ultraviolet light, and less attracted by red or infrared light.\nPreviously, most outdoor high-intensity lighting—such as for street lights and parking lots—used sodium vapor lamps, a type of lamp that is very efficient for large lights but also produces a characteristic orange glow. Many cities and countries, however, are moving towards using light-emitting diodes (LEDs) in outdoor lighting, since they have become affordable and highly efficient. LED lights promise to require less maintenance and cost less to operate. Also, they are not limited to the orange glow of sodium vapor lamps—they can produce whiter light. Some research suggests this is beneficial for safety, since whiter light makes roads and sidewalks easier to see. But it can also have some harms.\nOne possible harmful effect is to insects. If LEDs can produce whiter or bluer light, they may be more attractive to insects at night, disrupting their circadian rhythms and potentially even leading to their deaths as they circle lights endlessly.\nThis data comes from a study intended to understand how what lights attract insects and what types of insects are most affected, to study the problem. The researchers set up light traps: each trap contained a light bulb and captured any insect attracted by the light to enter the trap.\nThe researchers conducted their experiment by setting up six light traps, each with a different light bulb. (One trap contained no bulb, as a control.) The traps were set up each evening at sunset and then checked in the morning after sunrise, and the insects collected identified and counted.\nTraps were set up at three different sites (one urban, two rural). Relevant weather conditions were recorded. The experiment was repeated 32 times: 16 times at the urban site and 8 times at each rural site. This allowed the researchers to rotate which location (within an individual site) each trap was placed in, and allowed them to collect data under varying weather and moon phases. (Since the moon is bright in the night sky, the presence of a bright moon could presumably affect the results.)"
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-    "text": "Submitting\nOnce you’ve filled in the template, you can email the template file, the data, and any cleaning scripts to the repository editor (currently Alex Reinhart).\nAlternately, submit a pull request to our repository on GitHub. Save the Qmd with a meaningful name and place it in one of the top-level category directories (like medicine/ or astronomy/). Place the data files in data/. Double-check that you’ve committed everything (and nothing extraneous) and open a pull request as usual."
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+    "text": "You’ve probably seen that insects seem to be attracted to artificial lights at night. Insects appear to be most attractive to blue and ultraviolet light, and less attracted by red or infrared light.\nPreviously, most outdoor high-intensity lighting—such as for street lights and parking lots—used sodium vapor lamps, a type of lamp that is very efficient for large lights but also produces a characteristic orange glow. Many cities and countries, however, are moving towards using light-emitting diodes (LEDs) in outdoor lighting, since they have become affordable and highly efficient. LED lights promise to require less maintenance and cost less to operate. Also, they are not limited to the orange glow of sodium vapor lamps—they can produce whiter light. Some research suggests this is beneficial for safety, since whiter light makes roads and sidewalks easier to see. But it can also have some harms.\nOne possible harmful effect is to insects. If LEDs can produce whiter or bluer light, they may be more attractive to insects at night, disrupting their circadian rhythms and potentially even leading to their deaths as they circle lights endlessly.\nThis data comes from a study intended to understand how what lights attract insects and what types of insects are most affected, to study the problem. The researchers set up light traps: each trap contained a light bulb and captured any insect attracted by the light to enter the trap.\nThe researchers conducted their experiment by setting up six light traps, each with a different light bulb. (One trap contained no bulb, as a control.) The traps were set up each evening at sunset and then checked in the morning after sunrise, and the insects collected identified and counted.\nTraps were set up at three different sites (one urban, two rural). Relevant weather conditions were recorded. The experiment was repeated 32 times: 16 times at the urban site and 8 times at each rural site. This allowed the researchers to rotate which location (within an individual site) each trap was placed in, and allowed them to collect data under varying weather and moon phases. (Since the moon is bright in the night sky, the presence of a bright moon could presumably affect the results.)"
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+    "text": "Data\nEach row of data represents one trap on one night, and records the light bulb used in that trap, its location, the weather conditions, and the insects collected in that trap.\n\nData preview\n\n bug-attraction.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nLight Type\nType of light bulb used to attract insects. No means no bulb at all; A, B, and C are all Philips light bulbs whose color can be adjusted by the user; LED is a commercial white LED bulb; and CFL is a commercial compact fluorescent bulb.\n\n\nModel Lamp\nMore detailed description of the light bulb. For A, B, and C, describes the color temperature (K) the bulbs were set to.\n\n\nLocation\nLocation the light trap was placed. Site codes starting with B are urban (the UCLA Botanical Garden); site codes L and S were rural locations (a UCLA field station and a UCLA ranch). Each site has six separate traps, designated by a number.\n\n\nSite\nSite. BG = botanical garden, LK = La Kretz field station, Stunt = stunt ranch.\n\n\nStandardized Moon\nStandardized variable representing the percentage of the moon visible on the night chosen (i.e., the moon phase).\n\n\nStandardized Mean Temp\nTemperature on the night, standardized.\n\n\nStandardized Mean Humidity\nHumidity on the night, standardized.\n\n\nStandardized Max Wind\nMaximum wind speed on the night, standardized.\n\n\n% Moon Visible\nPercentage of moon visible (moon phase).\n\n\nStart Tem\nTemperature at the beginning of the night (Celsius)\n\n\nEnd Temp\nTemperature at the end of the experiment (Celsius)\n\n\nMean Temp\nMean temperature (Celsius)\n\n\nHumid Start\nRelative humidity at the start of the experiment (%)\n\n\nHumid End\nRelative humidity at the end of the experiment (%)\n\n\nMean Humidity\nMean humidity (%)\n\n\nMax Wind\nMaximum recorded wind speed (m/s)\n\n\nIllumination\nIllumination produced by the light (lux)\n\n\nDiptera\nNumber of insects of the order Diptera captured in the trap\n\n\nHymenoptera\nNumber of insects of the order Hymenoptera captured in the trap\n\n\nHemiptera\nNumber of insects of the order Hemiptera captured in the trap\n\n\nPsocoptera\nNumber of insects of the order Psocoptera captured in the trap\n\n\nColeoptera\nNumber of insects of the order Coleoptera captured in the trap\n\n\nCollembola\nNumber of insects of the order Collembola captured in the trap\n\n\nArachnid\nNumber of insects of the order Arachnid captured in the trap\n\n\nThysanura\nNumber of insects of the order Thysanura captured in the trap\n\n\nIsoptera\nNumber of insects of the order Isoptera captured in the trap\n\n\nLepidoptera\nNumber of insects of the order Lepidoptera captured in the trap\n\n\nNeuroptera\nNumber of insects of the order Neuroptera captured in the trap\n\n\nLarave\nNumber of insects of the order Larave captured in the trap\n\n\nOrthoptera\nNumber of insects of the order Orthoptera captured in the trap\n\n\nUnident\nNumber of unidentified insects captured in the trap\n\n\nTotal\nTotal number of insects captured in the trap\n\n\nOther Orders\nNumber of insects of other identified orders captured in the trap"
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+    "text": "Questions\nThese are adapted from questions used in notes for 36-707.\n\nWe are interested in the total number of insects collected in each trap (the Total variable) and how it relates to the light bulb type (Light Type). We also know that the location (Location), moon phase (% Moon Visible), mean night temperature (Mean Temp), and mean humidity (Mean Humidity) might affect insect activity. Conduct an EDA of these variables to determine what relationships the predictors may have with the response.\nConsider the study design. Because six traps were placed out each night, each with a different bulb (or no bulb), the traps were exposed to the same location, moon phase, temperature, and humidity. No bulb type has data from moon phases that are systematically different from the other bulbs, or was used on nights that were warmer than those for the other bulbs, or anything like that. This is known as a balanced design.\nComment on what this design choice means. Can location, moon phase, mean temperature, or mean humidity be confounding variables that would bias our estimates of the effect of bulb type? Do we need to incorporate them into our models to obtain an unbiased estimate of the causal effect of bulb type?\nObtain the mean number of insects trapped by each bulb. Display the means in a table. Which bulb attracts the fewest insects?\nNote there is one missing count (recorded as NA) that you may need to remove.\nSelect a GLM for the total number of insects collected as a function of bulb type, location, moon phase, temperature, and humidity. Use your EDA to guide your model choice, and produce any necessary diagnostic plots to validate your choice.\nProduce a table of coefficients estimated from your final model, including standard errors. Interpret, in words, the meaning of the coefficient for temperature, giving a 95% confidence interval for the size of the effect.\nConsider a night where the moon is 60% visible, the humidity is 60%, and the mean temperature is 15 °C. (These are roughly the average values in the data.) Produce a table of the predicted number of insects trapped by each bulb type at the La Kretz station on a night like this, giving 95% confidence intervals for each prediction. Does your result match the table from part 3?"
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+    "title": "Insect attraction to light",
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+    "text": "References\nLongcore, Aldern, Eggers, Flores, Franco, Hirshfield-Yamanishi, Petrinec, Yan, and Barroso (2015). “Tuning the white light spectrum of light emitting diode lamps to reduce attraction of nocturnal arthropods”. Philosophical Transactions of the Royal Society B 370: 20140125. https://doi.org/10.1098/rstb.2014.0125"
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-    "text": "Ecologists and evolutionary biologists have long investigated links between diversity and geography. For example, as the area of a habitat increases, the number of different species found within it increases—a seemingly obvious finding, but the exact shape of the relationship has been found to relate to the rates of immigration and extinction in the habitat, predator-prey behaviors, and so on.\nA similar relationship would seem likely to exist within a species. A species that lives over a small area might have low genetic diversity, because individuals frequently interbreed and there is little opportunity for some individuals to evolve separately from others. A species that lives over a wide area might have high genetic diversity, both because the varied habitat in which it lives means different individuals experience different natural selection pressures, and because individuals far apart may never be able to interbreed.\nThe exact nature of this relationship could be used to test hypotheses about evolution and natural selection. This dataset comes from a paper that explored the relationship in birds, which have widely varying geographic ranges and hence could be a good group to work with."
+    "text": "The common bean (Phaseolus vulgaris) is a plant widely cultivated for its dry seeds—beans. The species has long been cultivated for food and has developed into many varieties; many common beans, such as kidney beans, pinto beans, navy beans, and wax beans, are all varieties of the common bean. Because there are many varieties within the species, there are many varieties of seed that farmers can choose to plant, and each variety is suited for different growing climates and for different markets.\nHowever, in countries like Turkey, most farmers do not use commercially produced seeds certified to be all of one specific variety. They use seeds they obtain from their own crops or from other farmers, which may contain many different varieties of bean. To sort out specific varieties would require a trained expert to carefully sort the beans—which would be quite difficult when planting a field takes many thousands of seeds!\nAs a result, there is some demand for systems that can automatically classify seeds by variety, for example by using images of the seeds. If seeds can be accurately sorted by computer, a machine could automatically sort seeds at high speed, allowing farmers to sort their crops, pick the best varieties for their fields and their markets, and sell their seeds to other farmers who want a specific variety.\nTo collect this data, researchers used a camera to take photographs of 13,611 beans of seven different varieties. (This study was conducted in Turkey, so the varieties are known by their Turkish names: Barbunya, Battal, Bombay, Calı, Dermason, Horoz, Tombul, Selanik and Seker.) The beans were of known variety. The researchers then used image processing code to extract features from the images, including the bean sizes and several measures of shape. The goal was to use these features to classify the variety of the beans; if this is possible, one could build a system using a camera and computer to automatically classify beans by variety."
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-    "text": "Ecologists and evolutionary biologists have long investigated links between diversity and geography. For example, as the area of a habitat increases, the number of different species found within it increases—a seemingly obvious finding, but the exact shape of the relationship has been found to relate to the rates of immigration and extinction in the habitat, predator-prey behaviors, and so on.\nA similar relationship would seem likely to exist within a species. A species that lives over a small area might have low genetic diversity, because individuals frequently interbreed and there is little opportunity for some individuals to evolve separately from others. A species that lives over a wide area might have high genetic diversity, both because the varied habitat in which it lives means different individuals experience different natural selection pressures, and because individuals far apart may never be able to interbreed.\nThe exact nature of this relationship could be used to test hypotheses about evolution and natural selection. This dataset comes from a paper that explored the relationship in birds, which have widely varying geographic ranges and hence could be a good group to work with."
+    "text": "The common bean (Phaseolus vulgaris) is a plant widely cultivated for its dry seeds—beans. The species has long been cultivated for food and has developed into many varieties; many common beans, such as kidney beans, pinto beans, navy beans, and wax beans, are all varieties of the common bean. Because there are many varieties within the species, there are many varieties of seed that farmers can choose to plant, and each variety is suited for different growing climates and for different markets.\nHowever, in countries like Turkey, most farmers do not use commercially produced seeds certified to be all of one specific variety. They use seeds they obtain from their own crops or from other farmers, which may contain many different varieties of bean. To sort out specific varieties would require a trained expert to carefully sort the beans—which would be quite difficult when planting a field takes many thousands of seeds!\nAs a result, there is some demand for systems that can automatically classify seeds by variety, for example by using images of the seeds. If seeds can be accurately sorted by computer, a machine could automatically sort seeds at high speed, allowing farmers to sort their crops, pick the best varieties for their fields and their markets, and sell their seeds to other farmers who want a specific variety.\nTo collect this data, researchers used a camera to take photographs of 13,611 beans of seven different varieties. (This study was conducted in Turkey, so the varieties are known by their Turkish names: Barbunya, Battal, Bombay, Calı, Dermason, Horoz, Tombul, Selanik and Seker.) The beans were of known variety. The researchers then used image processing code to extract features from the images, including the bean sizes and several measures of shape. The goal was to use these features to classify the variety of the beans; if this is possible, one could build a system using a camera and computer to automatically classify beans by variety."
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-    "text": "Data\nThis data includes 387 bird species, along with two measures of their genetic diversity, a measure of the size of their breeding range, their body mass, and whether they are migratory or resident birds. The breeding ranges vary in size from a just few islands in the Seychelles, for the Seychelles warbler (Acrocephalus sechellensis), to enormous ranges of the Southern Ocean for the grey-headed albatross (Thalassarche chrysostoma).\n\nData preview\n\n bird-diversity.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nSpecies\nBird species name\n\n\nFamily\nFamily of birds to which this species belongs\n\n\nHeterozygosity\nA measure of genetic richness. Birds (and most animals) have two copies of each chromosome; expected heterozygosity is the probability that for a randomly selected gene, these two copies will be different versions (alleles) of the gene.\n\n\nAllelic richness\nThe average number of different alleles (versions of a gene) for each gene.\n\n\nBreeding range size\nSize of the spatial range over which this bird breeds, in units of 10,000 square km\n\n\nBody mass\nAverage body mass of the species (grams)\n\n\nLatitude\nMid-point latitude of the species breeding range. (All latitudes are positive, even those in the southern hemisphere.)\n\n\nMigratory status\nIs this species migratory or does it stay resident in one area?\n\n\nReferences\nReferences for the data for this species"
+    "text": "Data\nThis dataset contains features from 13,611 beans.\n\nData preview\n\n dry-beans.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nArea\nArea of the bean in the image, measured as the number of pixels it covers in the image\n\n\nPerimeter\nThe circumference of the bean, measured as the length of its border in pixels\n\n\nMajorAxisLength\nThe length of the bean’s major axis in pixels, i.e. the longest line that can be drawn from end to end of the bean\n\n\nMinorAxisLength\nThe length of the bean’s minor axis in pixels, i.e. the longest line, perpendicular to the major axis, that can be drawn across the bean\n\n\nAspectRation\nThe ratio of major axis to minor axis length\n\n\nEccentricity\nEccentricity of an ellipse with the same second moments of area as the bean\n\n\nConvexArea\nNumber of pixels in the smallest convex polygon that contains the bean\n\n\nEquivDiameter\nThe diameter of the circle with the same area of the bean\n\n\nExtent\nRatio of the area of the bean to the area of the smallest rectangle that contains it\n\n\nSolidity\nThe ratio of the bean’s area to its convex area (i.e. Area/ConvexArea)\n\n\nroundness\n4πA / P², where A is the bean area and P its perimeter\n\n\nCompactness\nEd/L, where Ed is the EquivDiameter and L is the major axis length\n\n\nShapeFactor1\nMajor axis length divided by area\n\n\nShapeFactor2\nMinor axis length divided by area\n\n\nShapeFactor3\n4A/(πL²), where A is area and L is the major axis length\n\n\nShapeFactor4\n4A/(πLl), where A is area, L is major axis length, and l is minor axis length\n\n\nClass\nVariety of the bean"
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-    "text": "Questions\n\nBody size and latitude are thought to be related to genetic diversity for reasons unrelated to breeding range size. Regress heterozygosity against breeding range size, body mass, and latitude. Examine the shape of the relationship, which is thought not to be linear and suspected to have an asymptote. Does the relationship depend on whether the species are migratory? Compare this to the shape you find using allelic richness instead of heterozygosity as the outcome.\nSpecies in this dataset are grouped into families; by definitions, species within a family are more similar than species in two different families. This may have effects on the assumptions of the regression. What assumptions may be violated, and what effect would this have on your results? (Note that the original paper accounted for this effect by taking into account the species’ phylogenetic trees.)\nFit a hierarchical model to account for this effect, using family to define the hierarchy. How do your results change?"
+    "text": "Questions\n\nUsing a multiclass classifier—such as a neural network, multiclass support vector machine, or kNN—build a classifier to predict bean variety from the available features. Choose the features you use carefully; because some features are calculated from others, some types of classifier may not need all features. Also choose your validation strategy carefully to ensure you can estimate your final model’s accuracy.\nExplore different ways to measure the accuracy of the classifier. Examining the confusion matrix may be useful to see what it is doing, but you should also produce useful summaries, such as accuracy, precision, recall, F1 score, and so on. Choose summaries that would be the most useful to bean producers.\nWhich varieties are easiest to confuse with each other? Which are most obviously different? Perhaps you can look up the varieties to see if the results make sense; if your classifiers can’t distinguish between two bean varieties that appear very different, something might be wrong."
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     "section": "References",
-    "text": "References\nFan H, Zhang Q, Rao J, Cao J, Lu X (2019) Genetic diversity-area relationships across bird species. The American Naturalist, volume 194, number 5. https://doi.org/10.1086/705346\nFan H, Zhang Q, Rao J, Cao J, Lu X (2019) Data from: Genetic diversity-area relationships across bird species. Dryad Digital Repository. https://doi.org/10.5061/dryad.9p380m9"
+    "text": "References\nKoklu, M. and Ozkan, I.A., (2020), “Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques.” Computers and Electronics in Agriculture, 174, 105507. https://doi.org/10.1016/j.compag.2020.105507"
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-    "text": "The AnAge Database of Animal Ageing and Longevity is a “curated database of ageing and life history in animals” that was “primarily developed for comparative biology studies.” It contains information, compiled from hundreds of scientific papers, for over 4,200 species. It’s mainly meant for studies of aging, so for each species, it includes variables such as lifespan, age of sexual maturity, adult body mass, typical body temperature, and metabolic rate. We can use this data to learn more about the relationships between each variable and the key outcome variable: lifespan."
+    "text": "In the spider species Pisaura mirabilis, male spiders woo female spiders to mate by giving them gifts: a piece of food (like a freshly captured insect) wrapped in spider silk. If the gift is good, the female spider allows the male to mate.\nIt turns out that sometimes male spiders cheat. Instead of wrapping food in silk, they wrap something else, such as bits of a plant or an empty insect exoskeleton they’ve already eaten from. This saves effort, though it runs the risk of the female finding out and not accepting the gift; to avoid this, males wrap the gifts in many extra layers of silk, so they can mate before the female fully unwraps the gift and discovers what’s inside.\nResearchers wanted to know why male spiders would cheat. Do they only cheat if they are already starving and can’t spare the food for a gift, or do they cheat opportunistically regardless of how much food they have to spare?\nIn the experiment presented here, male spiders kept in a laboratory were fed different diets, and attributes of the spider and his gift were recorded to see if there was a connection between the spider’s food and condition and the type of gift he produced."
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-    "text": "The AnAge Database of Animal Ageing and Longevity is a “curated database of ageing and life history in animals” that was “primarily developed for comparative biology studies.” It contains information, compiled from hundreds of scientific papers, for over 4,200 species. It’s mainly meant for studies of aging, so for each species, it includes variables such as lifespan, age of sexual maturity, adult body mass, typical body temperature, and metabolic rate. We can use this data to learn more about the relationships between each variable and the key outcome variable: lifespan."
+    "text": "In the spider species Pisaura mirabilis, male spiders woo female spiders to mate by giving them gifts: a piece of food (like a freshly captured insect) wrapped in spider silk. If the gift is good, the female spider allows the male to mate.\nIt turns out that sometimes male spiders cheat. Instead of wrapping food in silk, they wrap something else, such as bits of a plant or an empty insect exoskeleton they’ve already eaten from. This saves effort, though it runs the risk of the female finding out and not accepting the gift; to avoid this, males wrap the gifts in many extra layers of silk, so they can mate before the female fully unwraps the gift and discovers what’s inside.\nResearchers wanted to know why male spiders would cheat. Do they only cheat if they are already starving and can’t spare the food for a gift, or do they cheat opportunistically regardless of how much food they have to spare?\nIn the experiment presented here, male spiders kept in a laboratory were fed different diets, and attributes of the spider and his gift were recorded to see if there was a connection between the spider’s food and condition and the type of gift he produced."
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     "section": "Data",
-    "text": "Data\nEach observation is one animal species. Note that many variables are missing for individual species, so you may need to restrict your analysis to complete observations.\n\nData preview\n\n anage.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nHAGRID\nA unique ID for each entry (the Human Ageing Genomic Resources ID)\n\n\nKingdom\nAlong with the following variables (phylum, class, order, family, genus, and species), this gives the taxonomic classification of the species. Species names are typically given as genus and species, e.g. Anaxyrus americanus is the species name for the American toad.\n\n\nPhylum\nPhylum of the species\n\n\nClass\nClass of the species\n\n\nOrder\nOrder of the species\n\n\nFamily\nFamily of the species\n\n\nGenus\nGenus of the species\n\n\nSpecies\nSpecies name\n\n\nCommon.name\nCommon name (i.e., the name used by ordinary people, not scientists) for the animal\n\n\nMaximum.longevity.yrs\nMaximum longevity (lifespan), in years\n\n\nBody.mass.g\nTypical adult body mass, in grams\n\n\nMetabolic.rate\nTypical resting metabolic rate (i.e., rate of energy use), in Watts\n\n\nTemperature\nTypical body temperature, in Kelvin"
+    "text": "Data\nIn each experiment, male spiders of roughly the same age were placed in small boxes which previously held sexually receptive female spiders, to make them think they should prepare a gift. The experimenters provided the males with a live cricket nymph nearby, which they could presumably use as a gift; if the male didn’t catch it within twenty minutes, or caught it but didn’t make a gift with it, the experiment ended and was tried again a day later.\nThe weight of the prey before and after it was made into a gift was measured, to see if the male ate some of it before wrapping it into a gift. The weight of the silk used to wrap the prey was also recorded. (In a few cases, the prey and silk couldn’t be separated, and weights are not recorded. You’ll have to exclude these cases.)\n\nData preview\n\n spider-lab-gifts.csv \n\n\n\n\n\n\n\nVariable descriptions\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nMALE ID\nUnique ID for each male spider\n\n\nFOOD TREATMENT\nThe diet the male was fed. HF: high food (two insects per week), LF: low food (one insect per week)\n\n\nN TRIAL\nThe number of the trial (e.g. 2 if it took two tries for the male to make a gift)\n\n\nPREY WEIGHT BEFORE TRIAL (mg)\nThe weight of the cricket nymph before the trial (milligrams)\n\n\nGift weight (mg)\nWeight of the gift produced (milligrams)\n\n\nSILK WEIGHT (mg)\nWeight of the silk used to wrap the gift (milligrams)\n\n\nPREY WEIGHT AFTER (gift - silk weight)\nWeight of the prey after it was used to make a gift (milligrams)\n\n\nPREY WEIGHT LOSS\nWeight lost by prey (e.g. by the male spider eating some of it before making it into a gift) (milligrams)\n\n\nMALE MASS BEFORE TRIAL (mg)\nMass of the male spider (milligrams)\n\n\nMALE CEPH WIDTH (mm)\nWidth of the male spider’s cephalothorax (millimeters)\n\n\nMALE R BODY INDEX\nSee Questions, below\n\n\nLATENCY TO WRAP (sec)\nTime elapsed between capturing the prey and starting to make a gift out of it (seconds)\n\n\nMALE MASS AFTER TRIAL (mg)\nMass of the male spider after making the gift (milligrams)\n\n\nMALE WEIGHT difference (bef-after)\nDifference between male weight (before - after)\n\n\nTOT WRAPPING DURATION (sec)\nTotal time the male spent wrapping the gift (seconds)"
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     "section": "Questions",
-    "text": "Questions\n\nSome research has suggested that slowing the metabolic rate—for example, by intermittent fasting—may increase lifespan. Is this supported by the evidence? Develop a model for lifespan using metabolic rate, and interpret what it means.\nIs the relationship between metabolic rate and lifespan nonlinear, even after transformations? Use a nonparametric model to determine if a nonlinear fit is more appropriate.\nBuild a model to predict longevity using the available data. Evaluate its accuracy and report on any problems with the model.\nExplain whether your model supports the conclusion that changing an animal’s metabolic rate, such as through medical intervention, would cause its mean lifespan to change."
+    "text": "Questions\n\nDoes the male body weight (before the experiment) differ significantly between males fed the two different diets? What about their cephalothorax width?\nIf the prey lost weight, the male cheated and ate some of the prey before making a gift. Is the weight loss of the prey positively related to the mass of silk used to wrap it into a gift, as we’d expect if the male was hiding an inferior gift? Produce supporting graphs. Does the relationship depend on whether the male was fed the high or low food diet?\n(In three cases, the prey gained weight. According to the researchers, this is “most likely due to spiders’ injection of digestive fluids that serve for liquefying the prey during feeding”.)\nDoes the latency before wrapping the fit correlate with the weight loss of the gift, suggesting males waited longer to wrap inferior gifts (or waited too long and got tempted by the juicy cricket)?\nIs there a difference in the weight loss of the prey between males fed the high food and low food diets? What about in the amount of silk used?\nRegress the total time spent wrapping the gift against the mass of the gift. How many milligrams of silk can the male wrap per second?\nAccording to the researchers, the MALE R BODY INDEX variable was calculated as “the residuals of a regression of body mass on body size (cephalothorax width)”. Perform the regression yourself and try to match your results against the researchers. Does it match?"
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     "section": "References",
-    "text": "References\nThe AnAge Database of Animal Ageing and Longevity, at https://genomics.senescence.info/species/index.html. Data made available under the Creative Commons Attribution 3.0 Unported License."
+    "text": "References\nGhislandi PG, Beyer M, Velado P, Tuni C (2017). Silk wrapping of nuptial gifts aids cheating behaviour in male spiders. Behavioral Ecology 28 (3), 744-749. https://doi.org/10.1093/beheco/arx028\nGhislandi P, Beyer M, Velado P, Tuni C (2017). Data from: Silk wrapping of nuptial gifts aids cheating behaviour in male spiders. Dryad Digital Repository. https://doi.org/10.5061/dryad.kb1fh"
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-    "text": "Race and policing are some of the most inescapable topics in today’s America. There are large political arguments over the proper role of police, the effect of their tactics on historically disadvantaged groups, and their perceived biases. These controversies are amplified by a number of police killings of unarmed people, particularly African Americans, which have led to massive protests over the excessive use of force and sometimes to the prosecution of the officers involved.\nThere is no central government database recording all police uses of force, or even all killings by police officers. Several journalistic and advocacy groups have attempted to fill the gaps by compiling government data and news reporting to build large datasets including police killings and the circumstances surrounding them.\nThis dataset, Mapping Police Violence, is compiled by the advocacy group Campaign Zero. It builds on other databases, such as one compiled by the Washington Post, to include information from news reporting and other sources to provide additional context about each death. It is the most comprehensive dataset detailing all kinds of police violence incidents and helps us understand the scale of police violence and the differences across race/ethnicity and different geographical locations.\nWe can also combine this with the 2020 Census data on the county level to understand the prevalence of police violence and racial/ethnic differences in prevalence in different counties. The methodology is last updated on Oct 3, 2022, and the dataset is downloaded on Aug 7, 2023."
+    "text": "The World Bank compiles a database of World Development Indicators, which compile data from various sources intended to give the most current and accurate data about countries worldwide. Over a thousand different variables are available, updated yearly.\nThis dataset compiles a selection of 40 interesting variables for 266 countries and regions across 10 years, from 2013 to 2022."
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-    "text": "Race and policing are some of the most inescapable topics in today’s America. There are large political arguments over the proper role of police, the effect of their tactics on historically disadvantaged groups, and their perceived biases. These controversies are amplified by a number of police killings of unarmed people, particularly African Americans, which have led to massive protests over the excessive use of force and sometimes to the prosecution of the officers involved.\nThere is no central government database recording all police uses of force, or even all killings by police officers. Several journalistic and advocacy groups have attempted to fill the gaps by compiling government data and news reporting to build large datasets including police killings and the circumstances surrounding them.\nThis dataset, Mapping Police Violence, is compiled by the advocacy group Campaign Zero. It builds on other databases, such as one compiled by the Washington Post, to include information from news reporting and other sources to provide additional context about each death. It is the most comprehensive dataset detailing all kinds of police violence incidents and helps us understand the scale of police violence and the differences across race/ethnicity and different geographical locations.\nWe can also combine this with the 2020 Census data on the county level to understand the prevalence of police violence and racial/ethnic differences in prevalence in different counties. The methodology is last updated on Oct 3, 2022, and the dataset is downloaded on Aug 7, 2023."
+    "text": "The World Bank compiles a database of World Development Indicators, which compile data from various sources intended to give the most current and accurate data about countries worldwide. Over a thousand different variables are available, updated yearly.\nThis dataset compiles a selection of 40 interesting variables for 266 countries and regions across 10 years, from 2013 to 2022."
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+    "title": "World Development Indicators",
     "section": "Data",
-    "text": "Data\nEach row represents one death in an incident of police violence. There are 11,861 deaths recorded from 2012 to 2023.\nMapping Police Violence include detailed descriptions of each incident, including date and victim’s age, race, gender, and an image. It also includes more details about the incident including the location to the street address, the cause of death (can be multiple), circumstances, agency responsible, initial reasons, officer names and history, and so on.\nThe Census data provides 2020 demographic information for each county. It can be joined with the police violence data using the FIPS column, which uniquely identifies counties, though some incidents of violence do not have a county recorded or the county could not be matched to a FIPS code.\nNot all variables are available for all incidents. For older incidents, some data around prosecution (and its result) for the officer could be available.\n\nData preview\n\n mapping-police-violence.csv.gz \n\n\n\n\n\n\n county-census-2020.csv.gz \n\n\n\n\n\n\n\nVariable Descriptions\nThe dataset codebook from Mapping Police Violence and the U.S. Census Bureau documentation contain the full list of variables. This is an extract of a few useful variables from both tables. Note that many variables can have multiple values, separated by commas, in the Mapping Police Violence dataset.\n\nMapping Police Violence\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nname\nName of victim killed by law enforcement\n\n\nage\nAge of victim at time of death\n\n\ngender\nVictim’s indicated gender by news/official reports\n\n\nrace\nRace of victim according to news/official reports\n\n\ndate\nDate of lethal action on victim, in MM/DD/YYYY format\n\n\ncity\nCity where incident took place\n\n\nstate\nState where incident took place\n\n\ncounty\nCounty where incident took place\n\n\nFIPS\nFIPS code identifying the county where the incident took place. Not available when county is not given, if county is spelled incorrectly, or in most incidents in Connecticut and Alaska, which have unusual county systems\n\n\nagency_responsible\nAgency of law enforcement officer who took lethal action and responsible for killing\n\n\ncause_of_death\nCause of death of the victim, such as gunshot, Taser, vehicle, or drowning. Multiple reasons can be separated by commas\n\n\nsigns_of_mental_illness\nIndicates whether official/news reports mentioned any signs of mental illness or mental health crises: Yes/No/Unknown/Drug or Alcohol Use\n\n\nallegedly_armed\nIndicates whether victim was armed according to news/official sources: Allegedly Armed/Unarmed/Unclear/Vehicle\n\n\noff_duty_killing\nIndicates whether officer was working in an official capacity at the time lethal action was taken\n\n\nencounter_type\nInitial reported reason(s) for police to be on the scene prior to using deadly force\n\n\nofficer_names\nName of officer responsible for highest level of force\n\n\nofficer_races\nRace of officer responsible for highest level of force\n\n\nofficer_known_past_shootings\nIndicates if officer has had a previous incident of police violence in the database\n\n\n\n\n\nCensus Annual County Resident Population Estimates by Age, Sex, Race, and Hispanic Origin\n\n\n\n\n\n\n\nVariable\nDescription\n\n\n\n\nSTNAME\nState Name\n\n\nCTYNAME\nCounty Name\n\n\nFIPS\nFIPS code identifying the county\n\n\nTOT_POP\nTotal population\n\n\nNHWA_MALE/NHWA_FEMALE\nNot Hispanic, White alone male/female population\n\n\nNHBA_MALE/NHBA_FEMALE\nNot Hispanic, Black or African American alone male/female population\n\n\nNHIA_MALE/NHIA_FEMALE\nNot Hispanic, American Indian and Alaska Native alone male/female population\n\n\nNHAA_MALE/NHAA_FEMALE\nNot Hispanic, Asian alone male/female population\n\n\nNHNA_MALE/NHNA_FEMALE\nNot Hispanic, Native Hawaiian and Other Pacific Islander alone male/female population\n\n\nNHTOM_MALE/NHTOM_FEMALE\nNot Hispanic, Two or More Races male/female population\n\n\nH_MALE/H_FEMALE\nHispanic male/female population"
+    "text": "Data\nEach row represents one country, territory, or region in one year.\nThe World Bank includes some geographic groupings, such as “Sub-Saharan Africa” and “Pacific island small states”, as well as individual countries.\nNot all variables are available for all countries in all years, and more recent data is missing more often than older data.\n\nData preview\n\n world-bank.csv \n\n\n\n\n\n\n\nVariable descriptions\nThese descriptions are adapted directly from the World Bank’s data documentation. Codes in parentheses (such as SE.COM.DURS) are the World Bank codes for these variables. Search for these codes in the data series listed in the DataBank to find detailed descriptions of the sources and definitions for each variable.\n\n\n\nVariable\nDescription\n\n\n\n\nCountry Name\nName of country or region\n\n\nCountry Code\nUnique three-letter code identifying the country\n\n\nRegion\nGeographic region the country is in\n\n\nIsCountry\n1 if this row represents a country, 0 if it is an aggregated region (like Sub-Saharan Africa or all high-income countries)\n\n\nIncome group\nCountry’s income group as of 2023 (high-income, upper-middle-income, lower-middle-income, or low-income), based on its gross national income (GNI), as defined by the World Bank\n\n\nYear\nYear the values were observed in\n\n\nAlcohol\nTotal alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age) (SH.ALC.PCAP.LI)\n\n\nBattleDeaths\nBattle-related deaths (number of people) (VC.BTL.DETH)\n\n\nBirth\nBirth rate, crude (per 1,000 people) (SP.DYN.CBRT.IN)\n\n\nBirthSex\nSex ratio at birth (male births per female births) (SP.POP.BRTH.MF)\n\n\nCO2Emissions\nCO2 emissions (metric tons per capita) (EN.ATM.CO2E.PC)\n\n\nCompulsoryEducation\nCompulsory education, duration (years) (SE.COM.DURS)\n\n\nDeath\nDeath rate, crude (per 1,000 people) (SP.DYN.CDRT.IN)\n\n\nDeathsCD\nDeaths caused by communicable diseases and maternal, prenatal and nutrition conditions (% of total deaths) (SH.DTH.COMM.ZS)\n\n\nDeathsNCD\nDeaths caused by non-communicable diseases (% of total) (SH.DTH.NCOM.ZS)\n\n\nDensity\nPopulation density (people per sq. km of land area) (EN.POP.DNST)\n\n\nDiabetes\nDiabetes prevalence (% of population ages 20 to 79) (SH.STA.DIAB.ZS)\n\n\nElectricity\nAccess to electricity (% of population) (EG.ELC.ACCS.ZS)\n\n\nFertility\nFertility rate, total (births per woman) (SP.DYN.TFRT.IN)\n\n\nFixedTelephone\nFixed telephone subscriptions (IT.MLT.MAIN)\n\n\nForestArea\nForest area (% of land area) (AG.LND.FRST.ZS)\n\n\nGDP\nGross domestic product (current US$) (NY.GDP.MKTP.CD)\n\n\nGenderEducation\nGender parity index (GPI) for tertiary education enrollment: ratio of women to men enrolled (SE.ENR.TERT.FM.ZS)\n\n\nGenderEquality\nCPIA gender equality rating, measuring whether the government has programs and institutions to promote gender equality in education, health, the economy, and the law (1=low to 6=high) (IQ.CPA.GNDR.XQ)\n\n\nGovernmentExpenditure\nGeneral government final consumption expenditure (constant 2015 US$) (NE.CON.GOVT.KD)\n\n\nHomicide\nIntentional homicides (per 100,000 people) (VC.IHR.PSRC.P5)\n\n\nIncome\nAdjusted net national income (constant 2015 US$) (NY.ADJ.NNTY.KD)\n\n\nInternet\nIndividuals using the Internet (% of population) (IT.NET.USER.ZS)\n\n\nLandArea\nLand area (sq. km) (AG.LND.TOTL.K2)\n\n\nLegalRights\nStrength of legal rights index (0=weak to 12=strong) (IC.LGL.CRED.XQ)\n\n\nLiteracy\nLiteracy rate, adult total (% of people ages 15 and above) (SE.ADT.LITR.ZS)\n\n\nMilitary\nMilitary expenditure (% of general government expenditure) (MS.MIL.XPND.ZS)\n\n\nMobile\nMobile cellular subscriptions (IT.CEL.SETS)\n\n\nPM2.5\nPM2.5 air pollution, mean annual exposure (micrograms per cubic meter) (EN.ATM.PM25.MC.M3)\n\n\nPlaneDepartures\nNumber of takeoffs of planes operated by air carriers registered in this country, domestically or from foreign airports (IS.AIR.DPRT)\n\n\nPlanePassengers\nNumber of passengers carried by air carriers registered in this country (IS.AIR.PSGR)\n\n\nPoliticalStability\nPolitical stability and absence of violence/terrorism score (normalized as a z score) (PV.EST)\n\n\nPopulation\nPopulation, total (SP.POP.TOTL)\n\n\nPoverty\nPercentage of population who are multidimensionally poor (% of total population) (SI.POV.MDIM)\n\n\nRural\nRural population (% of total population) (SP.RUR.TOTL.ZS)\n\n\nRuralArea\nRural land area (sq. km) (AG.LND.TOTL.RU.K2)\n\n\nSuicide\nSuicide mortality rate (per 100,000 population) (SH.STA.SUIC.P5)\n\n\nTaxRevenue\nTax revenue (% of GDP) (GC.TAX.TOTL.GD.ZS)\n\n\nUnemployment\nUnemployment, total (% of total labor force) (modeled ILO estimate) (SL.UEM.TOTL.ZS)\n\n\nUrban\nUrban population (% of total population) (SP.URB.TOTL.IN.ZS)\n\n\nUrbanArea\nUrban land area (sq. km) (AG.LND.TOTL.UR.K2)"
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+    "href": "politics/world-bank.html#questions",
+    "title": "World Development Indicators",
     "section": "Questions",
-    "text": "Questions\n\nDo some EDA. What’s the age and gender distribution of victims? What are the most common encounter types in the dataset? What are the most common causes of death? Explore these questions, and others that seem interesting in the data, with charts and graphs.\nOne main question we would like to explore is that whether the demographic composition of the locality affects the occurrences of police violence. We can use the Census data to provide information on 7 groups (non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, non-Hispanic American Indian/Alaskan Native, non-Hispanic Native Hawaiian or other Pacific Islanders, non-Hispanic two or more races, and Hispanic) in each county. Build a model predicting the count of police violence in each county in the dataset based on their racial/ethnic minority percentage and other factors you think might be relevant. What does the result suggest?\nAnother argument people often make is that officers responsible for police violence against minorities often bear no consequences for their actions. Based on this data, build a logistic model to predict the likelihood of the police officer(s) responsible being charged with all factors you think might be relevant. What does the result suggest?\nSome people suggest that signs of mental health issues and/or drug use might also affect how police respond to a person, say we categorize any mentioning of a gunshot in the cause of death as an indicator for a higher level of force used. Does the data seem to support this claim?\nSimilarly, does the perception of the individual being armed appear to influence the extent of force employed by the police?"
+    "text": "Questions\nMany questions can be explored with this dataset. The questions below are intended merely as a starting point; instructors and students can undoubtedly find many other interesting relationships to explore.\n\nExplore the differences between income groups. For example, does the percentage of people using the Internet vary between income groups? Use graphical EDA or an ANOVA to study the differences, picking one year to focus on.\n(Fertility, alcohol consumption, forest area, and other variables may also be interesting to compare by income group.)\nExplore the relationship between Internet access and political stability for all countries in 2016. Is there a positive or negative relationship? Fit a regression model and interpret the coefficients.\nNow control for the country’s income group. Test whether an interaction term is necessary. Interpret the results. What does this interaction imply? Use a plot of the relationships to illustrate your results.\nMany other relationships between variables could be explored in EDA, regression, or ANOVA. For instance:\n\nCarbon dioxide emissions and PM 2.5 exposure, and how this relationship depends on GDP or income group\nGender parity in education (GenderEducation) and gender equality promotion by government policy (GenderEquality)\nThe strength of legal rights (LegalRights) and political instability and violence (PoliticalStability)\nDeaths in battle (BattleDeaths) and political stability (PoliticalStability)\nYears of compulsory education (CompulsoryEducation) and literacy rate (Literacy)\n\nOther variables may change over time in interesting ways, such as airplane travel (PlaneDepartures and PlanePassengers), forest area (ForestArea), and diabetes prevalence (Diabetes).\nFor any research question involving hypothesis tests, consider: What population can this data be said to be drawn from? Are tests necessary to generalize to that population?"
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-    "href": "politics/mapping-police-violence.html#references",
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+    "href": "politics/world-bank.html#references",
+    "title": "World Development Indicators",
     "section": "References",
-    "text": "References\nPolice violence data aggregated by Mapping Police Violence. Data retrieved on August 7, 2023.\nCensus data from the US Census Bureau’s County Population by Characteristics data. Technical documentation."
+    "text": "References\nThe World Development Indicators were downloaded from the DataBank service at https://databank.worldbank.org/source/world-development-indicators. All values are for the years 2013-2022. World Bank data is provided under a Creative Commons Attribution 4.0 International License."
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diff --git a/social/science-forums.html b/social/science-forums.html
index b955cee..60c996d 100644
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       <div class="sidebar-item-container"> 
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="false" aria-label="Toggle section">
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  <span class="menu-text">US College Scorecard</span></a>
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@@ -226,13 +243,13 @@
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
  <span class="menu-text">Medicine</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
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-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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@@ -255,13 +272,13 @@
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
   <a href="../psychology/cmu-sleep.html" class="sidebar-item-text sidebar-link">
@@ -354,13 +371,13 @@
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         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="true">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="true">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="true" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-8" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-9" class="collapse list-unstyled sidebar-section depth1 show">  
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   <div class="sidebar-item-container"> 
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diff --git a/submit-data.html b/submit-data.html
index a7ddbc6..93a0497 100644
--- a/submit-data.html
+++ b/submit-data.html
@@ -198,7 +198,7 @@
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- <span class="menu-text">Daily Life</span></a>
+ <span class="menu-text">Crime and Justice</span></a>
           <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="false" aria-label="Toggle section">
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       <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 ">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
+  <a href="./crime-and-justice/connecticut-stops.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Traffic stops in Connecticut</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <div class="sidebar-item-container"> 
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false">
+ <span class="menu-text">Daily Life</span></a>
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+            <i class="bi bi-chevron-right ms-2"></i>
+          </a> 
+      </div>
+      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
   <a href="./daily-life/college-scorecard.html" class="sidebar-item-text sidebar-link">
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+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
  <span class="menu-text">Medicine</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
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-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 ">  
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
  <span class="menu-text">Money</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-5" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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-      <ul id="quarto-sidebar-section-5" class="collapse list-unstyled sidebar-section depth1 ">  
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
  <span class="menu-text">Politics</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-6" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-6" class="collapse list-unstyled sidebar-section depth1 ">  
+      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
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   <div class="sidebar-item-container"> 
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
  <span class="menu-text">Psychology</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-7" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
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           </a> 
       </div>
-      <ul id="quarto-sidebar-section-7" class="collapse list-unstyled sidebar-section depth1 ">  
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-            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false">
+            <a class="sidebar-item-text sidebar-link text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false">
  <span class="menu-text">Social</span></a>
-          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-8" aria-expanded="false" aria-label="Toggle section">
+          <a class="sidebar-item-toggle text-start collapsed" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-9" aria-expanded="false" aria-label="Toggle section">
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           </a> 
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