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 # Introduction
 
-<img src="resources/images/01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_gd422c5de97_0_0.png" alt="Title image: Reproducibility in Cancer Informatics" width="100%" />
+<img src="01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_gd422c5de97_0_0.png" alt="Title image: Reproducibility in Cancer Informatics" width="100%" />
 
 ## Target Audience  
 
@@ -15,15 +15,15 @@ _This course is written for individuals who:_
 - Have not had formal training in computational methods.  
 - Have limited or no familiar with GitHub, Docker, or package management tools.
 
-<img src="resources/images/01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_g106226cdd08_0_0.png" alt="Intro to Reproducibility: For individuals who: Have some familiarity with R or Python - have written some scripts. Have not had formal training in computational methods. Have limited or no familiarity with GitHub. Advanced Reproducibility: For individuals who: Have completed the intro course and/or Have used GitHub somewhat." width="100%" />
+<img src="01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_g106226cdd08_0_0.png" alt="Intro to Reproducibility: For individuals who: Have some familiarity with R or Python - have written some scripts. Have not had formal training in computational methods. Have limited or no familiarity with GitHub. Advanced Reproducibility: For individuals who: Have completed the intro course and/or Have used GitHub somewhat." width="100%" />
 
 ## Topics covered:
 
 This is a two part series:
 
-<img src="resources/images/01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_g106742f3b7b_0_168.png" alt="Discussed in the Introductory Reproducibility in Cancer Informatics course: Organizing your project, using notebooks, Making your project open source with GitHub, using notebooks, managing package versions, writing durable code, documenting analyses, understanding the importance of code review. " width="100%" />
+<img src="01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_g106742f3b7b_0_168.png" alt="Discussed in the Introductory Reproducibility in Cancer Informatics course: Organizing your project, using notebooks, Making your project open source with GitHub, using notebooks, managing package versions, writing durable code, documenting analyses, understanding the importance of code review. " width="100%" />
 
-<img src="resources/images/01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_g106742f3b7b_0_189.png" alt="Discussed in the sequel course: Advanced Reproducibility for Cancer Informatics: Getting comfortable with GitHub concepts and workflow. Utilizing version control. Engaging in code review. Using a Docker image. Modifying a Docker image. Using Automation (github actions)." width="100%" />
+<img src="01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_g106742f3b7b_0_189.png" alt="Discussed in the sequel course: Advanced Reproducibility for Cancer Informatics: Getting comfortable with GitHub concepts and workflow. Utilizing version control. Engaging in code review. Using a Docker image. Modifying a Docker image. Using Automation (github actions)." width="100%" />
 
 ## Motivation
 
@@ -37,7 +37,7 @@ Equipping researchers with the skills to create reproducible data analyses incre
 
 This course introduces the concepts of reproducibility and replicability in the context of cancer informatics. It uses hands-on exercises to demonstrate in practical terms how to increase the reproducibility of data analyses. The course also introduces tools relevant to reproducibility including analysis notebooks, package managers, git and GitHub.
 
-<img src="resources/images/01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_gd422c5de97_0_10.png" alt="This course will demonstrate how to: Understanding why usability and documentation is vital, Identifying your user community, Building documentation and tutorials to maximize the usability of developed tools, Obtaining feedback from your users" width="1250" />
+<img src="01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_gd422c5de97_0_10.png" alt="This course will demonstrate how to: Understanding why usability and documentation is vital, Identifying your user community, Building documentation and tutorials to maximize the usability of developed tools, Obtaining feedback from your users" width="1250" />
 
 The course includes hands-on exercises for how to apply reproducible code concepts to their code. Individuals who take this course are encouraged to complete these activities as they follow along with the course material to help increase the reproducibility of their analyses.
 
@@ -47,7 +47,7 @@ Equip learners with reproducibility skills they can apply to their existing anal
 **What is not the goal**  
 This course is meant to introduce learners to the reproducibility tools, but _it does not necessarily represent the absolute end-all, be-all best practices for the use of these tools_. In other words, this course gives a starting point with these tools, but not an ending point. The advanced version of this course is the next step toward incrementally "better practices".
 
-<img src="resources/images/01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_g1006ff8e7e9_48_3.png" alt="Reproducibility is on a continuum. Goal of the course is to move learner’s skills toward creating reproducible analyses. This graph shows a two sided arrow with a gradient. On the very left is a ‘not repeatable analysis’ it was ran once. To the right of that is an analysis that ‘re-runs sometimes’. To the right of this, is an analysis that ‘Re-runs reliably in most contexts’.  And all the way to the right is a ‘perfectly reproducible analysis’ that ‘Re-runs in every situation and gets the same result every time’. In red lettering we note that every analysis is started by being run once but no analysis is ‘perfectly reproducible’." width="1250" />
+<img src="01-intro_files/figure-html//1LMurysUhCjZb7DVF6KS9QmJ5NBjwWVjRn40MS9f2noE_g1006ff8e7e9_48_3.png" alt="Reproducibility is on a continuum. Goal of the course is to move learner’s skills toward creating reproducible analyses. This graph shows a two sided arrow with a gradient. On the very left is a ‘not repeatable analysis’ it was ran once. To the right of that is an analysis that ‘re-runs sometimes’. To the right of this, is an analysis that ‘Re-runs reliably in most contexts’.  And all the way to the right is a ‘perfectly reproducible analysis’ that ‘Re-runs in every situation and gets the same result every time’. In red lettering we note that every analysis is started by being run once but no analysis is ‘perfectly reproducible’." width="1250" />
 
 ## How to use the course