Part 3 - Data Analysis

Group 1

1. Data Cleaning and Transformation

   • What does it mean to you to "clean" data, and where do you put this data?

   • How do you incorporate and document manual steps?

2. Analyses

   • What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
   • To what extent does a tool, such as Git and GitHub, address these challenges?
   • What are some pros/cons of sharing your analysis code?

Group 2

1. Data Cleaning and Transformation

   • What does it mean to you to "clean" data, and where do you put this data?

     • addressing missing data

     • correct format - string or numeric data

     • outliers

     • dealing with image dataset - filtering anything that is not trustable

     • metadata

     • raw data and clean data in the different folder-

     • what data is raw data?

   • How do you incorporate and document manual steps?
     • comments
     • write codes for the future us
     • tracing
     • version control

2. Analyses

   • What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
   • To what extent does a tool, such as Git and GitHub, address these challenges?
   • What are some pros/cons of sharing your analysis code?

     • Cons

       • The code works for me but not for others
       • Setting up the environment is easy for me and hard for others
         • python venv vs conda
           • matlab is better
       • Because I've shared my code, I can call it a day, you can just read the code and understand it
       • Takes time/effort to comment every line of code

     • Pros

       • I can see what my teammate has done and understand more about the project
       • Papers that share code help me to build my own research on top of it
       • Consistency of naming strategies
       • Forces you to clean up your processes
         • You want to make it as painless as possible for the next person

Group 3

1. Data Cleaning and Transformation

   • What does it mean to you to "clean" data, and where do you put this data?
   • How do you incorporate and document manual steps?

2. Analyses

   • What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
   • To what extent does a tool, such as Git and GitHub, address these challenges?
   • What are some pros/cons of sharing your analysis code?

Group 4

1. Data Cleaning and Transformation

   • What does it mean to you to "clean" data, and where do you put this data?
   • How do you incorporate and document manual steps?

2. Analyses

   • What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
   • To what extent does a tool, such as Git and GitHub, address these challenges?
   • What are some pros/cons of sharing your analysis code?

Group 5

1. Data Cleaning and Transformation
   • What does it mean to you to "clean" data, and where do you put this data?
   • How do you incorporate and document manual steps?

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2. Analyses
   • What are some challenges you've faced, or foresee facing, when it comes to documenting how you've analyzed data?
   • To what extent does a tool, such as Git and GitHub, address these challenges?
   • What are some pros/cons of sharing your analysis code?