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diff --git a/.nojekyll b/.nojekyll
@@ -1 +1 @@
-8a0b8cb6
+c5de491a
diff --git a/day_3.html b/day_3.html
@@ -174,7 +174,7 @@ <h1 class="title">Day 3 - Getting data in a programmatic way</h1>
 <tbody>
 <tr class="odd">
 <td>09:00am - 10:00am</td>
-<td><em>Writing a capstone proposal (60min)</em></td>
+<td><em>Writing a capstone proposal, Lisa Leombruni (60min)</em></td>
 </tr>
 <tr class="even">
 <td>10:00am - 11:00am</td>

diff --git a/index.html b/index.html
@@ -298,7 +298,7 @@ <h3 class="anchored" data-anchor-id="daily-schedule-subject-to-change">Daily sch
 <h2 class="anchored" data-anchor-id="learning-objectives">Learning objectives</h2>
 <p>The goal of EDS 214 - Analytical Workflows and Scientific Reproducibility is to expose incoming MEDS students to “good enough” practices of scientific programming develop skills in environmental data science to produce reproducible research. By the end of the course, students should be able to:</p>
 <ul>
-<li><p><strong>Develop knowledge in scientific analytical workflows</strong> To learn how to make your data-riven research reproducible, it is important to develop scientific workflows that will be relying on programming to accomplish the necessary tasks to go from the raw data to the results of your analysis (figures, new data, publications, …). Scripting languages, even better open ones such as <code>R</code> and <code>python</code>, are well-suited for scientists to develop reproducible scientific workflows, but are not the only tools you will need to develop reproducible and collaborative workflows</p></li>
+<li><p><strong>Develop knowledge in scientific analytical workflows</strong> to learn how to make your data-riven research reproducible, it is important to develop scientific workflows that will be relying on programming to accomplish the necessary tasks to go from the raw data to the results of your analysis (figures, new data, publications, …). Scripting languages, even better open ones such as <code>R</code> and <code>python</code>, are well-suited for scientists to develop reproducible scientific workflows, but are not the only tools you will need to develop reproducible and collaborative workflows</p></li>
 <li><p><strong>Learn how to code in a collaborative manner</strong> by practicing techniques such as code review and pair programming. Become comfortable asking for and conducting code review using <code>git</code> and <code>GitHub</code> to create pull request, ask feedback from peers, and merge changes into the main repository. Practice pair programming to cement the collaborative development of reproducible analytical workflows</p></li>
 <li><p><strong>Practice documenting code and data</strong> in a systematic way that will enable your collaborators, including your future self, to understand and reuse your work</p></li>
 </ul>

diff --git a/search.json b/search.json
@@ -179,7 +179,7 @@
     "href": "index.html#learning-objectives",
     "title": "EDS 214: Analytical Workflows and Scientific Reproducibility",
     "section": "Learning objectives",
-    "text": "Learning objectives\nThe goal of EDS 214 - Analytical Workflows and Scientific Reproducibility is to expose incoming MEDS students to “good enough” practices of scientific programming develop skills in environmental data science to produce reproducible research. By the end of the course, students should be able to:\n\nDevelop knowledge in scientific analytical workflows To learn how to make your data-riven research reproducible, it is important to develop scientific workflows that will be relying on programming to accomplish the necessary tasks to go from the raw data to the results of your analysis (figures, new data, publications, …). Scripting languages, even better open ones such as R and python, are well-suited for scientists to develop reproducible scientific workflows, but are not the only tools you will need to develop reproducible and collaborative workflows\nLearn how to code in a collaborative manner by practicing techniques such as code review and pair programming. Become comfortable asking for and conducting code review using git and GitHub to create pull request, ask feedback from peers, and merge changes into the main repository. Practice pair programming to cement the collaborative development of reproducible analytical workflows\nPractice documenting code and data in a systematic way that will enable your collaborators, including your future self, to understand and reuse your work"
+    "text": "Learning objectives\nThe goal of EDS 214 - Analytical Workflows and Scientific Reproducibility is to expose incoming MEDS students to “good enough” practices of scientific programming develop skills in environmental data science to produce reproducible research. By the end of the course, students should be able to:\n\nDevelop knowledge in scientific analytical workflows to learn how to make your data-riven research reproducible, it is important to develop scientific workflows that will be relying on programming to accomplish the necessary tasks to go from the raw data to the results of your analysis (figures, new data, publications, …). Scripting languages, even better open ones such as R and python, are well-suited for scientists to develop reproducible scientific workflows, but are not the only tools you will need to develop reproducible and collaborative workflows\nLearn how to code in a collaborative manner by practicing techniques such as code review and pair programming. Become comfortable asking for and conducting code review using git and GitHub to create pull request, ask feedback from peers, and merge changes into the main repository. Practice pair programming to cement the collaborative development of reproducible analytical workflows\nPractice documenting code and data in a systematic way that will enable your collaborators, including your future self, to understand and reuse your work"
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@@ -284,7 +284,7 @@
     "href": "day_3.html",
     "title": "Day 3 - Getting data in a programmatic way",
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-    "text": "Time (PST)\nActivity\n\n\n\n\n09:00am - 10:00am\nWriting a capstone proposal (60min)\n\n\n10:00am - 11:00am\nIntroduction to APIs (50min)\n\n\n11:00am - 11:10am\nBreak (10 min)\n\n\n11:10am - 12:30am\nAPIs hands-on (80 min)\n\n\n12:30pm - 1:30pm\nLunch (60 min)\n\n\n1:30pm - 2:00pm\nIntroduction to the group project (30 min)\n\n\n2:00pm - 4:00pm\nGroup Projects working session (120min)"
+    "text": "Time (PST)\nActivity\n\n\n\n\n09:00am - 10:00am\nWriting a capstone proposal, Lisa Leombruni (60min)\n\n\n10:00am - 11:00am\nIntroduction to APIs (50min)\n\n\n11:00am - 11:10am\nBreak (10 min)\n\n\n11:10am - 12:30am\nAPIs hands-on (80 min)\n\n\n12:30pm - 1:30pm\nLunch (60 min)\n\n\n1:30pm - 2:00pm\nIntroduction to the group project (30 min)\n\n\n2:00pm - 4:00pm\nGroup Projects working session (120min)"
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