ReproRehab2023_POD1_Playground

This is a repository prepared for ReproRehab2023 POD1 learners.

To learn more about ReproRehab, please visit www.reprorehab.usc.edu.

There's also a GitHub repository: github.com/reprorehab/reprorehab2023

POD1 focused on learning how to use MATLAB for your research. Here's a list of materials presented and discussed over the 7 weeks. All materials are in the folders of this repository as well.

Week 1: Tools for reproducible science I

• What is 'metadata'?
• How can one prepare metadata? (ft. JSON)
• Git/GitHub: a tool for reproducibility

activity : generate a JSON file using a MATLAB script, upload the file to a forked repo, and make a pull request to the upstream.

Week 2: Tools for reproducible science II

• Different ways to collaborate using Git/GitHub (forking, branching, inviting collaborators)
• GitHub Desktop: how to push commits from a local machine

activity : push commits from local machines using GitHub Desktop, create a new branch in a repository and merge the new branch with the main.

Week 3: FAIR principles and MATLAB data types

• What is FAIR (data) principles? (video: 'Unit3: Introduction to FAIR Data' of ReproRehab)
• Data types of MATLAB: numeric, character/string, categorical, table

activity : Run a MATLAB script, understand what lines of code mean, and modify several lines as requested.

All work pushed to the upstream repository. (MATLAB script used: S1_Simulate_Adaptation_Data.m prepared by Dr. James Finley)

Week 4: MATLAB tips to improve productivity

• Understanding different data types more: char and string
• Different functions/features to work with table arrays (ex. varfun, rowfun, function handle)
• Memory preallocation: a demo on its usefulness
• format specifier: what is it, and how to use it

activity : transform a csv output of Vicon Motion Capture System to have organized column names of a table. The activity highlighted...

using detectImportOptions to read csv files in MATLAB effectively

accessing and modifying a table's variable names using table Properties

using cellfun with function handles to apply changes to multiple items of a cell array

writing a for-loop to run an iterative job.

Week 5: Visualization in MATLAB

• Learning how to visualize data in MATLAB
• Configuring plots by setting parameters in advance

activity : modify a MATLAB script to add simulated data and plot the simulated data

extra_activity : complete another MATLAB script to read files in different folders, compile them to one struct and save it

(MATLAB script used: CompileJoingAngleData.m prepared by Dr. James Finley)

(DATASET used: https://doi.org/10.6084/m9.figshare.c.4494755)

Week 6: Doing statistical analysis in MATLAB

• Learning how to calculate group statistics, run one-way ANOVA, repeated-measures ANOVA and fit a linear mixed model to data
• Learning how to visulize analysis results (ex. using * to indicate significant difference)

activity : complete a MATLAB script to read multiple csv files, run repeated measures ANOVA, and plot the results

Week 7: How to write functions using inputParser (Optional)

• Learning how to write custom functions to perform a task
• Understanding the mechanism of inputParser

activity : complete a MATLAB function script to pass test cases; the activity also introduced different tips discussed in the previous weeks.

TA Tutorial: Tips for writing functions

This tutorial is prepared using MATLAB's Live Scripts (.mlx). Subtopics are:

• Global variables: what are they and how are they used
• Workspaces: what are they and how to work with different workspaces
• Different types of functions: nested, anonymous, & local
• Ways to handle errors

All questions related to each week's content or activity will be directed to Devin Austin ([email protected]) or Jinseok Oh ([email protected])